Evaluating alternative capacity strategies in semiconductor manufacturing under uncertain demand and price scenarios

Abstract

Because product demand is very volatile and machine and process technologies are advanced at a rapid pace, capacity planning, and investment in the semiconductor industry is a challenging task. Many studies have addressed mid- and short-term machine portfolio planning tasks of capacity planning. However, capacity planning should be considered in a framework of strategy planning in order to address the whole problem. This paper describes a method of analysis for adapting capacity strategy to changing business environment. The paper has three major parts. In the first part, empirical data analysis of a case study company yields predictive formulas for production and capacity costs. The uncertainty of demand is also calibrated using the geometric Brownian motion process. In the second part, experiments are designed to simulate demand and profit scenarios for comparing two capacity strategies. In the third part, the resultant data of simulation are analysed to gain insights to the relative performance of the two strategies in various scenarios. The analysis method provides a framework for formulating capacity strategy and for integrating capacity planning with business planning.