Integrated data envelopment analysis and neural network model for forecasting performance of wafer fabrication operations

Abstract

With the rapid change of manufacturing environments, semiconductor fabricators are forced to make continuous investments in advanced technology to maintain their competitive advantage. Wafer fabrication (fab) performance evaluations are important for examining past operations such as the capacity and resource allocation. In addition, forecasting provides useful information for what-if analyses to anticipate useful strategies early to avoid potential losses. However, the integration of performance evaluation and forecast based on a consideration of the relative performance along the time horizon has rarely been addressed. In particular, the part of performance evaluation is to generate the relative performance in a period with rolling data. Then, the forecast part is to build a model for performance prediction based on the result of present performance. This study aimed to construct a performance forecast model by integrating data envelopment analysis and a back-propagation neural network for performance evaluation and forecast, respectively. Empirical data from a leading semiconductor company in Taiwan was used to test the proposed model. The results provide basic information and early alarms to adjust the resource allocation before the future performance declines. The empirical analysis demonstrated the practical feasibility of the proposed approach.