Journal of Applied Sciences

Year: 2013 | Volume: 13 | Issue: 20 | Page No.: 4336-4339
DOI: 10.3923/jas.2013.4336.4339

Research on Demand Prediction Based on Supply Chain Management

Xin-Wu Li

Abstract: Demand prediction based on supply chain management performance for fresh agricultural products is one of the key techniques and a research hotspot in supply chain management. In order to overcome the deficiencies of traditional models, a new BP neural network algorithm for demand prediction of supply chain management of fresh agricultural products is presented based on the analysis of present literatures in the field. First the model structure of the presented algorithm is designed and simplified which combines the advantages of BP neural network and particle swarm optimization algorithm and the presented algorithm is improved through improving its inertia weight and constriction factor and calculation steps. Finally, the data from certain fresh agricultural product enterprise is taken for example to verify the validity and feasibility of the model and the experimental results show that the model can improve prediction accuracy and improve calculation efficiency when used in demand prediction of supply chain management practically.

Keywords: Supply chain management, demand prediction, BP neural network algorithm, particle swarm algorithm and fresh agricultural products

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