Adaptive neuro-fuzzy inference system for combined forecasts in a panel manufacturer

Improving the accuracy of demand forecasting has become a primary concern for a thin-film transistor liquid crystal display manufacturer. To address this concern, we develop a demand forecasting methodology that combines market and shipment forecasts. We investigate the weights assigned to the combination of forecasts using three linear methods (the minimum values of the forecast error, the adaptive weights and the regression analysis), as well as two nonlinear methods (fuzzy neural network and adaptive network based fuzzy inference system). A real data set from a panel manufacturer in Taiwan is used to demonstrate the application of the proposed methodology. The results show that the adaptive network based fuzzy inference system method outperforms other four methods. Also, we find that the mean absolute percent error (MAPE) of forecasting accuracy using the adaptive network based fuzzy inference system method can be improved effectively.