Fuzzy back-propagation network for PCB sales forecasting

Abstract

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 printed circuit board 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, aggregated and corresponding input parameters when fed into the FBPN. The proposed system is evaluated through the real life data provided by a printed circuit board company. Model evaluation results for research indicate that the Fuzzy back-propagation outperforms the other three different forecasting models in MAPE. © Springer-Verlag Berlin Heidelberg 2005.