| Grant number: | 18/19571-1 |
| Support type: | Regular Research Grants |
| Duration: | July 01, 2019 - September 30, 2021 |
| Field of knowledge: | Applied Social Sciences - Economics - Agrarian and Natural Resource Economics |
| Principal researcher: | Andréa Leda Ramos de Oliveira |
| Grantee: | Andréa Leda Ramos de Oliveira |
| Home Institution: | Faculdade de Engenharia Agrícola (FEAGRI). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Assoc. researchers: | Akebo Yamakami ; Anibal Tavares de Azevedo ; Carlos Eduardo Souza Moreira ; Karina Braga Marsola ; Marlon Fernandes de Souza ; Stanley Robson de Medeiros Oliveira |
Abstract
The grain freight cost is one of the biggest obstacles for the Brazil's competitiveness in international scenario. The road freight still is majority adopted on agricultural products transporting, and not unusual, that is the only choice to carry them. The cost weight, felt by the agribusiness operators, which work with supply chains might be decreased after the adoption of a freight management system. A promising alternative to deal with this challenge is the use of data mining techniques, which are able to extract patterns and trends in large amounts of data, which is why they have been increasingly used to support management decision in different areas, in place of intuition and resolutions based on experience. Thus, the project major goal is to develop and implement an intelligent system for forecasting road freight prices for agricultural grains. Thus, the main objective of this project is to develop and implement an intelligent system for forecasting road freight rates for agricultural grains, which evaluates the efficiency of some data mining techniques, considering different classes of Machine Learning algorithms, namely: Artificial Neural Networks, Support Vector Machines, Decision Trees and Random Forests.The massive use of freight road in the logistic and grain distribution, reinforces the importance of predicting freight prices, therefore a system that could provide that kind of information to agribusiness managers and decision makers, would be critical on their daily activities. In a further project phase, it is being proposed the model supports periodic refinement, based on the database updates. Hence the freight predictions might be used to support contracts terms between transportation companies and agribusiness operators, support the establishment of a minimum freight table for the road sector, as well as targeting the most proper locations to establish grain storage warehouses, processing sites, and transshipment terminals. (AU)
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