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General Motors Increases Its Production Throughput

Author

Listed:
  • Jeffrey M. Alden

    (General Motors Corporation, Mail Code 480-106-359, 30500 Mound Road, Warren, Michigan 48090)

  • Lawrence D. Burns

    (General Motors Corporation, Mail Code 480-106-EX2, 30500 Mound Road, Warren, Michigan 48090)

  • Theodore Costy

    (General Motors Corporation, Mail Code 480-206-325, 30009 Van Dyke Avenue, Warren, Michigan 48090)

  • Richard D. Hutton

    (General Motors Europe, International Technical Development Center, IPC 30-06, D-65423 Rüsselsheim, Germany)

  • Craig A. Jackson

    (General Motors Corporation, Mail Code 480-106-359, 30500 Mound Road, Warren, Michigan 48090)

  • David S. Kim

    (Department of Industrial and Manufacturing Engineering, Oregon State University, 121 Covell Hall, Corvallis, Oregon 97331)

  • Kevin A. Kohls

    (Soar Technology, Inc., 3600 Green Court, Suite 600, Ann Arbor, Michigan 48105)

  • Jonathan H. Owen

    (General Motors Corporation, Mail Code 480-106-359, 30500 Mound Road, Warren, Michigan 48090)

  • Mark A. Turnquist

    (School of Civil and Environmental Engineering, Cornell University, 309 Hollister Hall, Ithaca, New York 14853)

  • David J. Vander Veen

    (General Motors Corporation, Mail Code 480-734-214, 30003 Van Dyke Road, Warren, Michigan 48093)

Abstract

In the late 1980s, General Motors Corporation (GM) initiated a long-term project to predict and improve the throughput performance of its production lines to increase productivity throughout its manufacturing operations and provide GM with a strategic competitive advantage. GM quantified throughput performance and focused improvement efforts in the design and operations of its manufacturing systems through coordinated activities in three areas: (1) it developed algorithms for estimating throughput performance, identifying bottlenecks, and optimizing buffer allocation, (2) it installed real-time plant-floor data-collection systems to support the algorithms, and (3) it established common processes for identifying opportunities and implementing performance improvements. Through these activities, GM has increased revenue and saved over $2.1 billion in over 30 vehicle plants and 10 countries.

Suggested Citation

  • Jeffrey M. Alden & Lawrence D. Burns & Theodore Costy & Richard D. Hutton & Craig A. Jackson & David S. Kim & Kevin A. Kohls & Jonathan H. Owen & Mark A. Turnquist & David J. Vander Veen, 2006. "General Motors Increases Its Production Throughput," Interfaces, INFORMS, vol. 36(1), pages 6-25, February.
    • Handle: RePEc:inm:orinte:v:36:y:2006:i:1:p:6-25
    DOI: 10.1287/inte.1050.0181
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    References listed on IDEAS

    as
    1. Papadopoulos, H. T. & Heavey, C., 1996. "Queueing theory in manufacturing systems analysis and design: A classification of models for production and transfer lines," European Journal of Operational Research, Elsevier, vol. 92(1), pages 1-27, July.
    2. Eginhard J. Muth, 1979. "The Reversibility Property of Production Lines," Management Science, INFORMS, vol. 25(2), pages 152-158, February.
    3. Stanley B. Gershwin, 1987. "An Efficient Decomposition Method for the Approximate Evaluation of Tandem Queues with Finite Storage Space and Blocking," Operations Research, INFORMS, vol. 35(2), pages 291-305, April.
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    Cited by:

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    2. Elahi, Mirza M. Lutfe & Rajpurohit, Karthik & Rosenberger, Jay M. & Zaruba, Gergely & Priest, John, 2015. "Optimizing real-time vehicle sequencing of a paint shop conveyor system," Omega, Elsevier, vol. 55(C), pages 61-72.
    3. Abolhassani, Amir & James Harner, E. & Jaridi, Majid, 2019. "Empirical analysis of productivity enhancement strategies in the North American automotive industry," International Journal of Production Economics, Elsevier, vol. 208(C), pages 140-159.
    4. Anandasivam Gopal & Manu Goyal & Serguei Netessine & Matthew Reindorp, 2013. "The Impact of New Product Introduction on Plant Productivity in the North American Automotive Industry," Management Science, INFORMS, vol. 59(10), pages 2217-2236, October.
    5. Zhiyang Jia & Liang Zhang & Jorge Arinez & Guoxian Xiao, 2016. "Performance analysis for serial production lines with Bernoulli Machines and Real-time WIP-based Machine switch-on/off control," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6285-6301, November.
    6. Seog-Chan Oh & Jaemin Shin, 2021. "The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model," Mathematics, MDPI, vol. 9(11), pages 1-21, June.
    7. David Scheinker & Margaret L. Brandeau, 2020. "Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities," Interfaces, INFORMS, vol. 50(3), pages 176-189, May.

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