Fuzzy decision support system for demand forecasting with a learning mechanism

In this paper, a new decision support system for demand forecasting DSS_DF is presented. A demand forecast is generated in DSS_DF by combining four forecasts values. Two of them are obtained independently, one by a customer and the other by a market expert. They represent subjective judgments on future demand, given as linguistic values, such as “demand is around a certain value” or “demand is not lower than a certain value”, etc. Two additional forecasts are crisp values, obtained using conventional statistical methods, one using time-series analysis based on decomposition (TSAD), and the other using an auto regressive integrated moving average (ARMA) model. The combination of these four forecast values into one improved forecast is made by applying fuzzy IF-THEN rules. A modified Mamdani-style inference is used, which enables reasoning with fuzzy inputs. A new learning mechanism is developed and incorporated into the DSS_DF to adapt the rule bases that combine the individual forecasted values. The rule bases are adapted taking into consideration the performance of each of the forecast methods recorded in the past. The application of DSS_DF is demonstrated by an illustrative example. The forecasts obtained by DSS_DF are compared with results procured by applying the conventional TSAD and ARMA methods separately. The results obtained are encouraging and indicate that combining forecasts obtained by different methods may be beneficial.