Abstract
A key to success for manufacturing company in the worldwide competition is to build a reliable and accurate forecasting model that can predict in time suitable items at sufficient quantity and adapt to an uncertain environment. This paper presents a novel approach by combining SOM and fuzzy rule base for sales forecasting. Independent variables related to sales’ variation are collected and fed into the SOM for classification. Then, corresponding fuzzy rule base is selected and applied for sales forecasting. Genetic process is further applied to fine-tune the composition of the rule base. Finally, using the simulated data, the effectiveness of the proposed method is shown by comparing with other approaches.
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References
Abdel-Aal, R.E., Al-Garni, A.Z.: Forecasting Monthly Electric Energy Consumption in Eastern Saudi Arabia Using Univariate Time Series Analysis. Energy 22, 1059–1069 (1997)
Chang, P.C., Hsieh, J.C.: A Neural Networks Approach for Due-Date Assignment in a Wafer Fabrication Factory. Int. J. of Industrial Engineering (July 2002)
Chow, T.W.S., Leung, C.T.: Nonlinear Autoregressive Integrated Neural Network Model for Short-Term Load Forecasting. IEE Proceeding Online no. 19960600, 500–506 (1996)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Hsu, P.H., Wang, C.H., Shyu, J.Z., Yu, H.C.: A Litterman BVAR Approach for Production Forecasting of Technology Industries. Technological Forecasting & Social Change 70, 67–82 (2002)
Kimoto, T., Asakawa, K.: Stock Market Prediction System with Modular Neural Network. In: IEEE International Joint Conference on Neural Network, pp. 1–6 (1990)
Kuo, R.J.: A Sales Forecasting System Based on Fuzzy Neural Network with Initial Weights Generated by Genetic Algorithm. European Journal of Operational Research 129, 496–517 (2001)
Law, R., Au, N.: A Neural Network Model to Forecast Japanese Demand for Travel to Hong Kong. Tourism Management 20, 89–97 (1999)
Luxh, J.T., Jens, Riis, O., Stensballe, B.: A Hybrid Econometric-Neural Network Modeling Approach fFor Sales Forecasting. The International Journal of Production Economics 43, 175–192 (1996)
Srinivasan, D.: Evolving Artificial Neural Networks for Short Term Load Forecasting. Neural computing 23, 265–276 (1998)
Tawfik, M.: Sales Forecasting Practices of Egyptian Public Enterprises: Survey Evidence. International Journal of Forecasting 16, 359–368 (2000)
Tawfiq, A.S., Ibrahim, E.A.: Artificial Neural Networks as Applied to Long-Term Demand Forecasting. Artificial Intelligence in Engineering 13, 189–197 (1999)
Yang, T.H.: A Study of Integrating Genetic Algorithm & Neural Network in Forecasting Problem of Printed Circuit Board Industry. Unpublished Master Thesis, Yuan Ze University, Taiwan (2004)
Yao, X.: Evolving Artificial Neural Networks. Proceedings of the IEEE 87, 1423–1447 (1999)
Wang, L.X.J., Mendel, M.: Generating Fuzzy Rules by Learning from Examples. IEEE Trans. on Syst. Man Cybernet. 22, 1414–1427 (1992)
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© 2005 Springer-Verlag Berlin Heidelberg
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Chang, PC., Lai, K.R. (2005). Combining SOM and Fuzzy Rule Base for Sale Forecasting in Printed Circuit Board Industry. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_150
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DOI: https://doi.org/10.1007/11427469_150
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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