Decentralizing semiconductor capacity planning via internal market coordination

Abstract

Semiconductor capacity planning is a cross-functional decision that requires coordination between the marketing and manufacturing divisions. We examine main issues of a decentralized coordination scheme in a setting observed at a major US semiconductor manufacturer: marketing managers reserve capacity from manufacturing based on product demands, while attempting to maximize profit; manufacturing managers allocate capacity to competing marketing managers so as to minimize operating costs while ensuring efficient resource utilization. This cross-functional planning problem has two important characteristics: (1) both demands and capacity are subject to uncertainty, and (2) all decision entities own private information while being self-interested. To study the issues of coordination we first formulate the local marketing and the manufacturing decision problem as separate stochastic programs. We then formulate a centralized stochastic programming model (JCA), which maximizes the firm's overall profit. JCA establishes a theoretical benchmark for performance, but is only achievable when all planning information is public. If local decision entities are to keep their planning information private, we submit that the best achievable coordination corresponds to an alternative stochastic model (DCA). We analyze the relationship and the theoretical gap between JCA and DCA, thereby establishing the price of decentralization. Next, we examine two mechanisms that coordinate the marketing and manufacturing decisions to achieve DCA using different degrees of information exchange. Using insights from the Auxiliary Problem Principle (APP), we show that under both coordination mechanisms the divisional proposals converge to the global optimal solution of DCA. We illustrate the theoretic insights using numerical examples as well as a real-world case.2

Managing Capacity in the High-Tech Industry: A Review of Literature

“…Although demand volatility is the main source of uncertainty in semiconductor tool capacity planning, yield variability, especially for new technologies, is an important consideration too. Karabuk and Wu (2002) incorporate both demand and yield uncertainty into a stochastic programming model via discrete scenarios. A scenario is defined as a combination of a vector of yield realization factor applied to expected wafer starts and a demand vector through the planning horizon.…”

Capacity reservation contracts for high-tech industry

“…In this paper, we consider capacity reservation contracts between a device manufacturer and her main customers: the customers reserve future capacity from the manufacturer, and the manufacturer expands capacity by configuring in-house capabilities or by making outsourcing arrangements. We do not consider capital expansion decisions in this context because capacity reservation is a means of order management, while physical expansion (e.g., building new facilities, procuring capital equipment) requires long-term strategic planning under multi-period market scenarios [14]. Physical expansion typically involves strategic decisions, which require different cost and demand information and thus different model settings [12].…”

Reverse auction-based job assignment among foundry fabs

“…In the context of contract manufacturing, Karabuk and Wu (2002) examined issues of a decentralized coordination scheme between capacity reservation by marketing managers and capacity allocation to competing marketing managers by manufacturing managers. The authors compared the gap between centralized and decentralized allocation models, evaluated the price of decentralization and proposed an information exchange principle.…”