Abstract
In a wafer fabrication facility (fab), cycle time, throughput, and utilization are strongly related to work-in-process (WIP). Past researchers have proved that maintaining a suitable number of WIP can get the maximum throughput and the minimum cycle time. Therefore, WIP is one of the most important performance indices in a fab. Based on the Theory of Constraints, managing bottleneck workstations is important for controlling the throughput of a system. In this study, we use the artificial neural network to find bottleneck workstations in a fab, and set their suitable WIP levels. A decision tree model is used to identify important non-bottleneck upstream workstations that are strongly related to bottleneck workstations, and then their influence on the bottleneck workstations are analyzed. Then some artificial neural network models for analyzing the WIP levels of bottleneck workstations and the total WIP in a fab are developed. These models can provide managers useful information for setting the suitable total number of WIP to maintain the required WIP levels for the bottleneck workstations and adjusting key non-bottleneck upstream workstations to reduce the total WIP level.