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Conference Papers Year : 2017

Neural Network System to Forecast the Soybean Exportation on Brazilian Port of Santos

Abstract

Agricultural products are an important part of the Brazilian economy. In soybean production, the country is the second largest producer with 114.0 million tons in the 2016/2017 harvest. Mato Grosso state is the largest Brazilian producer with 30.5 million tons and the port of Santos is mainly requested by being the largest port in Latin America. However, the poor infrastructure of the transport road causes bottlenecks when dispatching soybean through the major ports. Artificial Neural Networks (ANN) are used worldwide in logistics; therefore, we propose to design, train and simulate an ANN on MatLab$$\copyright $$ software to forecast the demand of soybean produced in Mato Grosso and exported through the port of Santos. The value of 9.0 million tons was predicted for 2017 as an increase of about 26.5% compared with the 2016 movement of 7.1 million tons. In addition, it was noticed that 5.9 million tons were moved only in the first five months (Jan–May) of transactions in 2017.
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hal-01707279 , version 1 (12-02-2018)

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Emerson Rodolfo Abraham, João Reis, Adriane Paulieli Colossetti, Aguinaldo Souza, Rodrigo Carlo Toloi. Neural Network System to Forecast the Soybean Exportation on Brazilian Port of Santos. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2017, Hamburg, Germany. pp.83-90, ⟨10.1007/978-3-319-66926-7_10⟩. ⟨hal-01707279⟩
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