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Business Intelligence for Logístics
 
The main focus of this line is to apply Business Intelligence technologies such as Data Warehousing and Data Mining, for supporting decision making processes within Logistics and Supply Chain Management domains.
 
 
Biofuels Logistics and Traceability
 
The 4 years-length Bioenergy Program of the UNCuyo is carried out by the Faculty of Engineering, Faculty of Applied Industrial Sciences, Faculty of Agronomy, and INTA (National Institute of Agricultural Technology), and sponsored by YPF (more information
about the program here).
 
The objective is to acquire knowledge about biofuels by means of research projects focused on energy crops, production processes, quality and logistics and traceability.
 
Logistics and Traceability have a fundamental role given the need
of producing and managing these new energetic resources in a sustainable and profitable way. Research topics include: identification of actors and composition of biofuels supply chain, design of an information system for biofuels traceability, application of Business Intelligence for identification of aspects with higher impact on quality and yield of the final product, and identification of socioeconomic impact of the introduction of biofuels industry in new regions.
   
Automatic learning of socio-economic impact models for agribusiness industries
 
The generation of new agribusinesses usually produces the introduction of new industries in different regions. This situation generates requirement of primary and secondary workforce to satisfy new industries demands, which may not be fully available in the region, because of the lack of skilled people.
 
In this scenario, usually happens that the region grows economically, producing changes in land use, employment and competence. Due to the consequences produced by 
this 
grow, prediction of these changes in demographic and economic factors of such regions is of paramount importance, especially considering that usually the land used for some agribusiness cannot be used simultaneously for another one, so there is competition for scarce resources.
 
Some models have been developed in the past for predicting
land use, such as Lowry-type models. However they do not predict other interesting factors, especially employment but also macro-economic and cultural factors. In this project regression and other forecasting machine learning techniques are being used for learning more complex prediction models.
 
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©2008 CEAL - Centro de Estudios y Aplicaciones Logísticas
Facultad de Ingeniería - Universidad Nacional de Cuyo
Centro Universitario - Mendoza - Argentina