Model predictive control strategies for supply chain management in semiconductor manufacturing

Wenlin Wang, Daniel Rivera, Karl G. Kempf

Research output: Contribution to journalArticle

90 Scopus citations

Abstract

This paper examines the application of model predictive control (MPC), an advanced control technique originating from the process industries, to supply chain management (SCM) problems arising in semiconductor manufacturing. The main goal of this work is to demonstrate the usefulness of MPC as a tactical decision policy that is an integral part of a comprehensive hierarchical decision framework aimed at achieving operational excellence. A fluid analogy is used to describe the dynamics of the supply chain. Compared to traditional flow control problems, challenges of SCM in semiconductor manufacturing result from high stochasticity and nonlinearity in throughput times, yields and customer demands. The advantages of the control-oriented receding horizon formulation behind MPC are presented for three benchmark problems which highlight distinguishing features of semiconductor manufacturing. The effects of tuning, model parameters, and capacity are shown by comparing system robustness and multiple performance metrics in each case study.

Original languageEnglish (US)
Pages (from-to)56-77
Number of pages22
JournalInternational Journal of Production Economics
Volume107
Issue number1
DOIs
StatePublished - May 1 2007

Keywords

  • Inventory management
  • Model predictive control
  • Production control
  • Semiconductor manufacturing
  • Supply chain management

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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