Capacity Planning in the Semiconductor Industry: Dual-Mode Procurement with Options

Abstract

T o help a firm reduce inefficiencies associated with equipment capacity planning, we propose a dual-mode equipment procurement (DMEP) framework. DMEP combines dual-source (i.e., a less-expensive-but-slower base mode and a faster-but-more-expensive flexible mode) procurement with option contracts in three layers: a contract negotiation layer, where the firm chooses the best combination of lead time and price for each mode from the supply contract menu; a capacity reservation layer, where the firm reserves total equipment procurement quantities from the two supply modes before the planning horizon starts; and an execution layer, where the firm orders equipment from the two supply modes based on the updated demand information. We first investigate the execution layer as a dynamic dual-source capacity expansion problem with demand backlogging and demonstrate that the optimal policy lacks structure even under the simplest setting. Thus, we propose a heuristic solution for the execution-layer problem, which also serves as a building block for the other two layers. Through numerical analysis, we quantify the value of the added flexibility of DMEP for the firm. The DMEP framework has been implemented at Intel Corporation and has resulted in savings of tens of millions of dollars for one process technology.

Exploring the economic consequences of letting a supplier hold reserve storage

“…However, the model used assumes stationary demand and because the optimal policy is found to be too complex, a heuristic order allocation policy is employed. The dualsource and finite horizon setting at the execution layer in the overall framework of Peng et al [9] is similar to ours. However, the focus of the analysis is on production capacity and heuristics rather than inventory levels and optimal policies, as in our model.…”

A Population-Growth Model for Multiple Generations of Technology Products

“…To the contrary, being able to characterize the demand and corresponding uncertainty enables the company to appropriately hedge against future risk. For example, Intel has implemented a method to use the knowledge of future demand uncertainty to optimally design option contracts with its equipment suppliers to reduce excess capacity (Peng et al 2012, Kempf et al 2013.…”

“…Next, the model needs to capture interactions among the products and account for the competition that Intel faces. Finally, the model should be able to estimate forecast uncertainty because the primary challenge of long-range planning is to mitigate the risk of future uncertainty (Peng et al 2012, Kempf et al 2013. In this paper, we abstract from the situation at Intel and develop a general demand model for multiple product generations and show its usefulness in long-range forecasting.…”

Reducing simulation model complexity by using an adjustable base model for path-based automated material handling systems - a case study in the semiconductor industry

“…The semiconductor sector with its extremely expensive manufacturing equipment is one of the most capital-intensive industries in the world (Peng et al 2012). Considering the increasing level of automation of handling systems, it is notably important to know about their systems' behavior because even small deficiencies can lead to massive losses (Terwiesch and E. Bohn 2001, Arzt and Bulcke 1999).…”