Model Predictive Control in Semiconductor Supply Chain Operations

  • Karl Kempf
  • Kirk Smith
  • Jay Schwartz
  • Martin Braun
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 152)


Maintaining agility in a multi-echelon multi-product multi-geography supply chain with long and variable manufacturing lead times, stochastic product yields, and uncertain demand is a difficult goal to achieve. The approach advocated here is based on a practical application of control theory that includes a model of the system being controlled, feedback from previous results, feed-forward based on demand forecasts, and optimization of both the financial results and the control actions applied to achieve them. This Model Predictive Control (MPC) approach has been employed in the continuous-flow process industry for many years, and has been independently suggested for supply chains by a number of academic research teams. This chapter describes a large-scale application of the approach in the semiconductor industry.


Supply Chain Forecast Error Inventory Level Model Predictive Control Demand Forecast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Three groups need to be acknowledged for collaboration in realizing the work described here. First is Dr. Daniel E. Rivera, Professor of Chemical Engineering and Program Director of the Control Systems Engineering Laboratory at Arizona State University. Professor Rivera was the first to suggest applying Model Predictive Control to supply chain problems at Intel. With the aid of three of his PhD students – Martin Braun, Wenlin Wang, and Jay Schwartz – he constructed the theoretical foundation for our work. Second are Dr. Joseph Lu, Chief Scientist and Senior Fellow at Honeywell International, Honeywell Process Solutions in Phoenix, Arizona and Dr. Duane Morningred, Engineering Fellow at Honeywell International, Honeywell Process Solutions in Moorpark, California. Together they facilitated the implementation of the initial Model Predictive Controller in Intel’s supply chain management system in collaboration with Intel’s Kirk Smith (formerly with Honeywell International). Third are Intel’s Customer Planning and Logistics Group (CPLG) Vice Presidents Keith Reese and Frank Jones. The former supported the early research and the latter the implementation of Model Predictive Control systems in Intel’s supply chain. Their strategic vision was vital to the success of the work described here. The critical contributions of tens of additional members of CPLG are gratefully acknowledged.


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Copyright information

© Springer New York 2011

Authors and Affiliations

  • Karl Kempf
    • 1
  • Kirk Smith
  • Jay Schwartz
  • Martin Braun
  1. 1.Decision Technologies GroupIntel CorporationChandlerUSA

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