Implementation of a Back-Propagation Neural Network for Demand Forecasting in a Supply Chain - A Practical Case Study

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

[1]  Mostafa Al Mamun,et al.  Artificial neural networks applied to long-term electricity demand forecasting , 2004, Fourth International Conference on Hybrid Intelligent Systems (HIS'04).

[2]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[3]  許鉅秉,et al.  國際期刊 Transportation Research-Part E---Logistics and Transportation Review 特刊編輯補助 , 2006 .

[4]  Yaman Barlas,et al.  Analysis of stock management gaming experiments and alternative ordering formulations , 2004 .

[5]  J. Holmström,et al.  Supply chain collaboration: making sense of the strategy continuum , 2005 .

[6]  Wei-Jen Lee,et al.  Neural network based demand forecasting in a deregulated environment , 1999 .

[7]  Ibrahim El-Amin,et al.  Artificial neural networks as applied to long-term demand forecasting , 1999, Artif. Intell. Eng..

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  Eleni Mangina,et al.  The changing role of information technology in food and beverage logistics management: beverage network optimisation using intelligent agent technology , 2005 .

[10]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[11]  Chandra Lalwani,et al.  Controllable, observable and stable state space representations of a generalized order-up-to policy , 2006 .

[12]  Richard Weber,et al.  Improved supply chain management based on hybrid demand forecasts , 2007, Appl. Soft Comput..

[13]  Yan Dong,et al.  TOWARDS BETTER COORDINATION OF THE SUPPLY CHAIN , 2001 .

[14]  Xiaolong Zhang,et al.  Delayed demand information and dampened bullwhip effect , 2005, Oper. Res. Lett..

[15]  Xiaolong Zhang.  The impact of forecasting methods on the bullwhip effect , 2004 .

[16]  Markos Papageorgiou,et al.  Long‐term sales forecasting using holt–winters and neural network methods , 2005 .

[17]  Janis Grabis,et al.  Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand , 2005, Eur. J. Oper. Res..

[18]  Ulrich Wilhelm Thonemann,et al.  Production, Manufacturing and Logistics Improving supply-chain performance by sharing advance demand information , 2002 .

[19]  D. Simchi-Levi,et al.  The impact of exponential smoothing forecasts on the bullwhip effect , 2000 .

[20]  Wen-Yau Liang,et al.  Agent-based demand forecast in multi-echelon supply chain , 2006, Decis. Support Syst..

[21]  C. Richard Cassady,et al.  Impact of cross-docking on inventory in a decentralized retail supply chain , 2006 .

[22]  M. Saito,et al.  Demand forecasting by the neural network with Fourier transform , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[23]  R. Law.  Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting , 2000 .

[24]  Rachel T. A. Croson,et al.  Upstream versus downstream information and its impact on the bullwhip effect , 2005 .