Capacity planning under demand uncertainty for semiconductor manufacturing

TLDR

Capacity planning in semiconductor manufacturing is challenging because tool requirements are highly sensitive to product mix and future demand uncertainty. The study proposes a stochastic integer programming approach to determine a robust tool set that accommodates demand uncertainty. The method models a finite set of demand scenarios with associated probabilities, then solves a budget‑constrained integer program that minimizes the weighted average unmet demand to select the optimal tool set. The resulting robust tool set performs well across all scenarios, achieving comparable or lower costs than a single‑profile plan.

Abstract

In the semiconductor industry, capacity planning, the calculation of number of tools needed to manufacture forecasted product demands, is difficult because of sensitivity to product mix and uncertainty in future demand. Planning for a single demand profile can result in a large gap between planned capacity and actual capability when the realized product mix turns out differently from the one planned. This paper presents a method which accepts this uncertainty and uses stochastic integer programming to find a tool set robust to changes in demand. It considers a set of possible, discrete demand scenarios with associated probabilities, and determines the tools to purchase, under a budget constraint, to minimize weighted average unmet demand. The resulting robust tool set deals well with all the scenarios at no or minimal additional cost compared to that for a single demand profile. We also discuss the modifications of conventional business processes, needed to implement this method for dealing explicitly with uncertainty in demand.