Strategic Capacity Planning in the Semiconductor Industry: a Stochastic Programming Approach

We study strategic capacity planning in the semiconductor industry. Working with a major US semiconductor manufacturer on the strategic configuration of their worldwide production capacities, we identify two unique characteristics of this problem as follows: (1) wafer demands and manufacturing capacity are both main sources of uncertainty, and (2) capacity planning must consider two distinct viewpoints: a product perspective concerning marketing and strategic demand management, and a process standpoint involving manufacturing, yield, and technology configuration. These two unique characteristics change, in a fundamental way, how strategic capacity planning problem should be approached. To describe this complex problem, we first formulate a multi-stage stochastic program with recourses where demand and capacity uncertainties are incorporated via a scenario structure. To reconcile the marketing and manufacturing perspectives to the problem, we consider a decomposition of the planning problem resembling decentralized decision-making involving the headquarter, the marketing manager, and the manufacturing manager. To study various trade-offs under this decentralized structure, we develop recourse approximation schemes simulating different decentralization strategies. These schemes vary in information requirements and complexity, while providing insight on the value of information in this environment. We conduct extensive experiments to analyze the characteristics of decisions under different levels of uncertainties, and assess the value of alternative schemes from the standpoint of computational requirements and solution quality. The results indicate that it is possible to arrive at near optimal solutions (within 6.5%) with information decentralization while using a fraction (less than 16.2%) of the computer time.