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SLIM: Short Cycle Time and Low Inventory in Manufacturing at Samsung Electronics

Author

Listed:
  • Robert C. Leachman

    (Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, California 94720–1777)

  • Jeenyoung Kang

    (IBM Korea, Inc., MMAA Building, Dogok-dong, Gangnam-gu, Seoul, Korea 467–12)

  • Vincent Lin

    (Leachman and Associates LLC, 5870 Carmel Way, Union City, California 94587)

Abstract

SLIM is a set of methodologies and scheduling applications for managing cycle time in semiconductor manufacturing. SLIM includes methodology for calculating target cycle times and target WIP levels for individual manufacturing steps, heuristic algorithms for factory floor scheduling, and optimization-based capacity analysis. Between 1996 and 1999, Samsung Electronics Corp., Ltd., implemented SLIM in all its semiconductor manufacturing facilities. It reduced manufacturing cycle times to fabricate dynamic random access memory devices from more than 80 days to less than 30. Considering the decline of selling prices for dynamic random access memory devices, SLIM enabled Samsung to capture an additional $1 billion in sales revenue compared to the revenue it would have realized had cycle times not been reduced.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:orinte:v:32:y:2002:i:1:p:61-77
    DOI: 10.1287/inte.32.1.61.15
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    Citations

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    Cited by:

    1. Payman Jula & Robert C. Leachman, 2010. "Coordinated Multistage Scheduling of Parallel Batch-Processing Machines Under Multiresource Constraints," Operations Research, INFORMS, vol. 58(4-part-1), pages 933-947, August.
    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. 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.
    4. Leachman, Robert C. & Ding, Shengwei, 2007. "Integration of speed economics into decision-making for manufacturing management," International Journal of Production Economics, Elsevier, vol. 107(1), pages 39-55, May.
    5. Toly Chen & Yu-Cheng Wang, 2014. "Enhancing the Effectiveness of Cycle Time Estimation in Wafer Fabrication-Efficient Methodology and Managerial Implications," Sustainability, MDPI, vol. 6(8), pages 1-22, August.
    6. Phillip O. Kriett & Sebastian Eirich & Martin Grunow, 2017. "Cycle time-oriented mid-term production planning for semiconductor wafer fabrication," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4662-4679, August.
    7. Payman Jula & Robert Leachman, 2008. "Coordinating decentralized local schedulers in complex supply chain manufacturing," Annals of Operations Research, Springer, vol. 161(1), pages 123-147, July.
    8. Yao, Shiqing & Jiang, Zhibin & Li, Na & Zhang, Huai & Geng, Na, 2011. "A multi-objective dynamic scheduling approach using multiple attribute decision making in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 130(1), pages 125-133, March.

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