Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry

Reliable prediction of sales can improve the quality of business strategy. In this research, fuzzy logic and artificial neural network are integrated into the fuzzy back-propagation network (FBPN) for sales forecasting in Printed Circuit Board (PCB) industry. The fuzzy back propagation network is constructed to incorporate production-control expert judgments in enhancing the model's performance. Parameters chosen as inputs to the FBPN are no longer considered as of equal importance, but some sales managers and production control experts are requested to express their opinions about the importance of each input parameter in predicting the sales with linguistic terms, which can be converted into pre-specified fuzzy numbers. The proposed system is evaluated through the real world data provided by a printed circuit board company and experimental results indicate that the Fuzzy back-propagation approach outperforms other three different forecasting models in MAPE measures.

[1]  E. H. Lloyd,et al.  Long-Term Storage: An Experimental Study. , 1966 .

[2]  Terence C. Mills,et al.  Time series techniques for economists , 1990 .

[3]  Pei-Chann Chang,et al.  Evolving neural network for printed circuit board sales forecasting , 2005, Expert Syst. Appl..

[4]  Madan M. Gupta,et al.  Fuzzy mathematical models in engineering and management science , 1988 .

[5]  Kunhuang Huarng,et al.  Heuristic models of fuzzy time series for forecasting , 2001, Fuzzy Sets Syst..

[6]  N. Dalkey,et al.  An Experimental Application of the Delphi Method to the Use of Experts , 1963 .

[7]  P. Chang,et al.  AN INVESTIGATION OF THE HYBRID FORECASTING MODELS FOR STOCK PRICE VARIATION IN TAIWAN , 2004 .

[8]  Ping-Teng Chang,et al.  The fuzzy Delphi method via fuzzy statistics and membership function fitting and an application to the human resources , 2000, Fuzzy Sets Syst..

[9]  Jens Ove Riis,et al.  A hybrid econometric—neural network modeling approach for sales forecasting , 1996 .

[10]  Pei-Chann Chang,et al.  A hybrid system combining self-organizing maps with case-based reasoning in wholesaler's new-release book forecasting , 2005, Expert Syst. Appl..

[11]  R. J. Kuo,et al.  A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights , 1998, Decis. Support Syst..

[12]  Shyi-Ming Chen,et al.  Handling forecasting problems using fuzzy time series , 1998, Fuzzy Sets Syst..

[13]  J. A. Chen,et al.  A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm , 2004, Expert Syst. Appl..

[14]  Toly Chen.  A fuzzy back propagation network for output time prediction in a wafer fab , 2003, Appl. Soft Comput..

[15]  Massimiliano Versace,et al.  Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks , 2004, Expert Syst. Appl..

[16]  Toly Chen,et al.  Forecasting methods using fuzzy concepts , 1999, Fuzzy Sets Syst..

[17]  Saifur Rahman,et al.  Peak load forecasting using a fuzzy neural network , 1995 .

[18]  Ingoo Han,et al.  Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index , 2000 .