NDLI: Implementation of a Back-Propagation Neural Network for Demand Forecasting in a Supply Chain

Content Provider	IEEE Xplore Digital Library
Author	Yun-Hui Cheng Liao Hai-Wei Yun-Shiow Chen
Copyright Year	2006
Description	Author affiliation: Ind. Eng. Dept., Yuan-Ze Univ., Chung-li (Yun-Hui Cheng; Liao Hai-Wei; Yun-Shiow Chen)
Abstract	Demand forecasting is a key way to the efficient management of SCM (supply chain management) in a logistics information system. A poor forecasting approach for the product demands in marketing must cause to decrease competitive capability, lose customers and increase costs. A real case of the product demand forecasting was studied by an artificial neural network (ANN) approach demonstrated in this paper. The studied case is a medium-scale electrical connectors production corporation in Taiwan, which manufactures a variety of the connectors to supply marketing needs of diverse assembly products including mobile telephone, TFT, PDA, CD-ROM, CD-RW, DVD-ROM, DVD-player, notebook computer, digital camera, etc.. The types of the connectors produced by the studied firm are over 50. Owing to the insufficient experimental data provided by the studied corporation, a simulation tool called AweSim was used to simulate the orders of the various types of connectors, according to the historical received orders, and a set of the simulated data was used to train the proposed back-propagation network (BPN) so as to offer a proper demand forecasting tool to the studied firm. Four BPN structures were trained and tested and the best one was determined by ANOVA analysis. The BPN demand forecasting has being used by the studied corporation
Starting Page	1036
Ending Page	1041
File Size	6530227
Page Count	6
File Format	PDF
ISBN	1424403170
DOI	10.1109/SOLI.2006.328894
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Back-propagation Network Supply chain management Computer aided manufacturing Computational modeling Supply chains Demand forecasting Artificial Intelligence Artificial neural networks Predictive models System Simulation Demand Forecasting Connectors Artificial Neural Network Neural networks Personal digital assistants
Content Type	Text
Resource Type	Article

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