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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 537675, 7 pages
http://dx.doi.org/10.1155/2013/537675
Research Article

An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

Research Center of Cluster and Enterprise Development, School of Business Administration, Jiangxi University of Finance & Economics, Nanchang 330013, China

Received 22 June 2013; Revised 8 September 2013; Accepted 10 October 2013

Academic Editor: Zhigang Jiang

Copyright © 2013 Wei He. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.