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Queueing Model Improves IBM's Semiconductor Capacity and Lead-Time Management

Author

Listed:
  • Steven M. Brown

    (IBM Systems and Technology Group, Hopewell Junction, New York 12533)

  • Thomas Hanschke

    (Department of Mathematics, Clausthal University of Technology, Clausthal-Zellerfeld, Germany)

  • Ingo Meents

    (IBM Deutschland Research and Development GmbH, Mainz, Germany)

  • Benjamin R. Wheeler

    (MIT Sloan School of Management, Cambridge, Massachusetts 02142)

  • Horst Zisgen

    (IBM Deutschland Research and Development GmbH, Mainz, Germany)

Abstract

At IBM's 300 mm semiconductor plant in East Fishkill, New York, we developed and implemented the Enterprise Production Planning and Optimization System (EPOS), an advanced queueing network model for capacity planning. EPOS extends earlier queueing network models by adding the ability to model product-specific batch service and batch arrivals and multichamber process equipment, and by embedding a linear program for allocating lots to the right queues when route choices are present. EPOS addresses both short-term tactical capacity planning and long-term strategic capital investment planning. It has become an integral part of predicting factory bottlenecks, prioritizing continuous-improvement efforts, planning capital equipment investments, and managing factory lead times. EPOS results have guided IBM's efforts to improve factory performance and reduce tens of millions of dollars of expense.

Suggested Citation

  • Steven M. Brown & Thomas Hanschke & Ingo Meents & Benjamin R. Wheeler & Horst Zisgen, 2010. "Queueing Model Improves IBM's Semiconductor Capacity and Lead-Time Management," Interfaces, INFORMS, vol. 40(5), pages 397-407, October.
    • Handle: RePEc:inm:orinte:v:40:y:2010:i:5:p:397-407
    DOI: 10.1287/inte.1100.0516
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    References listed on IDEAS

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    1. Robert C. Leachman & Jeenyoung Kang & Vincent Lin, 2002. "SLIM: Short Cycle Time and Low Inventory in Manufacturing at Samsung Electronics," Interfaces, INFORMS, vol. 32(1), pages 61-77, February.
    2. Stuart Bermon & Sarah Jean Hood, 1999. "Capacity Optimization Planning System (CAPS)," Interfaces, INFORMS, vol. 29(5), pages 31-50, October.
    3. Sungmin Park & John W. Fowler & Gerald T. Mackulak & J. Bert Keats & W. Matthew Carlyle, 2002. "D-Optimal Sequential Experiments for Generating a Simulation-Based Cycle Time-Throughput Curve," Operations Research, INFORMS, vol. 50(6), pages 981-990, December.
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    Cited by:

    1. S. K. Samanta & R. Nandi, 2021. "Queue-Length, Waiting-Time and Service Batch Size Analysis for the Discrete-Time GI/D-MSP (a,b) / 1 / ∞ $^{\text {(a,b)}}/1/\infty $ Queueing System," Methodology and Computing in Applied Probability, Springer, vol. 23(4), pages 1461-1488, December.
    2. Sujit Kumar Samanta & Kousik Das, 2023. "Detailed Analytical and Computational Studies of D-BMAP/D-BMSP/1 Queueing System," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-37, March.
    3. 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.

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