A framework for evaluating remote diagnostics investment decisions for semiconductor equipment suppliers

With advances in information technology, service activities for expensive equipment used in semiconductor manufacturing can be performed from a remote location. This capability is called remote diagnostics (RD). Currently, there are intense development efforts in the semiconductor industry for implementing RD in wafer fabrication facilities to reduce maintenance and capital costs and improve productivity. In this paper, we develop a queueing-location model to analyze the capacity and location problem of after sales service providers, considering the effects of RD technology. Our model optimizes the location, capacity and the type of service centers while taking congestion effects into consideration. We solve this model using a simulation optimization approach in which we use a genetic algorithm to search the solution space. We demonstrate how our methodology can be used in strategic investment planning regarding the adoption of RD technology and service center siting through a realistic case study.