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IBM Solves a Mixed-Integer Program to Optimize Its Semiconductor Supply Chain

Author

Listed:
  • Brian T. Denton

    (Division of Health Care Policy and Research, Mayo Clinic, Pavilion Building 3-06, Rochester, Minnesota 55906)

  • John Forrest

    (IBM T. J. Watson Research Center, PO Box 218, Yorktown Heights, New York 10598)

  • R. John Milne

    (IBM Systems and Technology Group, 1000 River Road, Essex Junction, Vermont 05452)

Abstract

IBM Systems and Technology Group uses operations research models and methods extensively for solving large-scale supply chain optimization (SCO) problems for planning its extended enterprise semiconductor supply chain. The large-scale nature of these problems necessitates the use of computationally efficient solution methods. However, the complexity of the models makes developing robust solution methods a challenge. We developed a mixed-integer programming (MIP) model and supporting heuristics for optimizing IBM’s semiconductor supply chain. We designed three heuristics, driven by practical applications, for capturing the discrete aspects of the MIP. We leverage the model structure to overcome computational hurdles resulting from the large-scale problem. IBM uses the model and method daily for operational and strategic planning decisions and has saved substantial costs.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:36:y:2006:i:5:p:386-399
    DOI: 10.1287/inte.1060.0238
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Ying-Ju Chen & Brian Tomlin & Yimin Wang, 2017. "Dual Coproduct Technologies: Implications for Process Development and Adoption," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 692-712, October.
    2. Peter C. Bell & Jing Chen, 2017. "Close integration of pricing and supply chain decisions has strategic as well as operations level benefits," Annals of Operations Research, Springer, vol. 257(1), pages 77-93, October.
    3. Mustapha Ouhimmou & Sophie D'Amours & Robert Beauregard & Daoud Ait-Kadi & Satyaveer Singh Chauhan, 2009. "Optimization Helps Shermag Gain Competitive Edge," Interfaces, INFORMS, vol. 39(4), pages 329-345, August.
    4. Wang, Chi-Tai & Chiu, Chui-Sheng, 2014. "Competitive strategies for Taiwan's semiconductor industry in a new world economy," Technology in Society, Elsevier, vol. 36(C), pages 60-73.
    5. Nazrul Shaikh & Vittal Prabhu & Danilo Abril & David Sánchez & Jorge Arias & Esteban Rodríguez & Germán Riaño, 2011. "Kimberly-Clark Latin America Builds an Optimization-Based System for Machine Scheduling," Interfaces, INFORMS, vol. 41(5), pages 455-465, October.
    6. 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.
    7. 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.

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