An effective fuzzy collaborative forecasting approach for predicting the job cycle time in wafer fabrication

Predicting the cycle time of each job in a factory is an important task to the factory. However, it is not easy to deal with the uncertainty in the job cycle time. To cope with this problem and to effectively predict the job cycle time, an effective fuzzy collaborative forecasting approach is proposed in this study. The main difference between the proposed methodology and the existing methods is that the proposed methodology generates a fuzzy cycle time forecast in an effective way. In addition, the proposed method utilizes each round of fuzzy artificial neural network training to generate the upper and lower bounds of the job cycle time. The upper and lower bounds then serve as the basis for the subsequent collaboration. We collected the data of 120 jobs from a wafer fabrication factory to assess the effectiveness of the proposed method. The analysis results showed that the proposed fuzzy collaborative forecasting approach was indeed more efficient and accurate than some existing methods. (C) 2013 Elsevier Ltd. All rights reserved.