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IBM Blends Heuristics and Optimization to Plan Its Semiconductor Supply Chain

Author

Listed:
  • Alfred Degbotse

    (IBM Corporation, Essex Junction, Vermont 05452)

  • Brian T. Denton

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Kenneth Fordyce

    (IBM Corporation, Hurley, New York 12443)

  • R. John Milne

    (School of Business, Clarkson University, Potsdam, New York 13699)

  • Robert Orzell

    (IBM Corporation, Essex Junction, Vermont 05452)

  • Chi-Tai Wang

    (Institute of Industrial Management, National Central University, Jhongli City 32001, Taiwan, Republic of China)

Abstract

IBM uses operations research techniques to plan its enterprise semiconductor supply chain. The scale and complexity of this planning problem make developing robust supply chain optimization tools a challenge. Pure optimization methods are computationally infeasible, and fast heuristic methods alone generate poor results. Consequently, we developed a method that decomposes the problem by dividing the bills of materials product structure horizontally and vertically into complex and simple portions that are based on the major stages in semiconductor manufacturing and the choices of supply chain paths for building parts. The method then solves the complex portions with a mixed-integer program and the simple portions with fast heuristics that contain small embedded linear programs. A unique pegging algorithm, an explosion heuristic, and an implosion linear program enable coordination among these portions. The result is a unified production, shipping, and distribution plan with no evidence of the original decomposition. This method has helped IBM to improve its asset utilization, customer service, and inventory levels.

Suggested Citation

  • Alfred Degbotse & Brian T. Denton & Kenneth Fordyce & R. John Milne & Robert Orzell & Chi-Tai Wang, 2013. "IBM Blends Heuristics and Optimization to Plan Its Semiconductor Supply Chain," Interfaces, INFORMS, vol. 43(2), pages 130-141, April.
    • Handle: RePEc:inm:orinte:v:43:y:2013:i:2:p:130-141
    DOI: 10.1287/inte.1120.0642
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    References listed on IDEAS

    as
    1. Frederix, Florent, 2001. "An extended enterprise planning methodology for the discrete manufacturing industry," European Journal of Operational Research, Elsevier, vol. 129(2), pages 317-325, March.
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    4. Brian T. Denton & John Forrest & R. John Milne, 2006. "IBM Solves a Mixed-Integer Program to Optimize Its Semiconductor Supply Chain," Interfaces, INFORMS, vol. 36(5), pages 386-399, October.
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    Citations

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    Cited by:

    1. Andreas Fink & Natalia Kliewer & Dirk Mattfeld & Lars Mönch & Franz Rothlauf & Guido Schryen & Leena Suhl & Stefan Voß, 2014. "Model-Based Decision Support in Manufacturing and Service Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(1), pages 17-24, February.
    2. Amy David & David Farr & Ross Januszyk & Urmila Diwekar, 2015. "USG Uses Stochastic Optimization to Lower Distribution Costs," Interfaces, INFORMS, vol. 45(3), pages 216-227, June.
    3. Darvish, Maryam & Coelho, Leandro C., 2018. "Sequential versus integrated optimization: Production, location, inventory control, and distribution," European Journal of Operational Research, Elsevier, vol. 268(1), pages 203-214.
    4. Uday Venkatadri & Shentao Wang & Ashok Srinivasan, 2021. "A Model for Demand Planning in Supply Chains with Congestion Effects," Logistics, MDPI, vol. 5(1), pages 1-24, January.
    5. Weskamp, Christoph & Koberstein, Achim & Schwartz, Frank & Suhl, Leena & Voß, Stefan, 2019. "A two-stage stochastic programming approach for identifying optimal postponement strategies in supply chains with uncertain demand," Omega, Elsevier, vol. 83(C), pages 123-138.

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