A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights

Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g., promotion, cause a sudden change in the sales pattern. Thus, this study utilizes fuzzy logic a proposed fuzzy neural network (FNN) for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the forecast from ANN using the time series data and the promotion length through the other ANN. Model evaluation results indicate that the proposed system can more accurately perform than the conventional statistical method and single ANN. (C) 1998 Elsevier Science B.V. All rights reserved.