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Learning curve analysis in total productive maintenance

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  • Wang, F. -K.
  • Lee, W.
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    Abstract

    The continuous improvement concepts such as total quality management, just-in-time and total productive maintenance have been widely recognized as a strategic weapon and successfully implemented in many organizations. In this paper, we focus on the application of total productive maintenance (TPM). A random effect non-linear regression model called the Time Constant Model was used to formulate a prediction model for the learning rate in terms of company size, sales, ISO 9000 certification and TPM award year. A two-stage analysis was employed to estimate the parameters. Using the approach of this study, one can determine the appropriate time for checking the performance of implementing total productive maintenance. By comparing the expected overall equipment effectiveness (OEE), one can improve the maintenance policy and monitor the progress of OEE.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 29 (2001)
    Issue (Month): 6 (December)
    Pages: 491-499

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    Handle: RePEc:eee:jomega:v:29:y:2001:i:6:p:491-499

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    Keywords: Learning curve Overall equipment effectiveness Total productive maintenance;

    References

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    1. Towill, Denis R., 1985. "Management systems applications of learning curves and progress functions," Engineering Costs and Production Economics, Elsevier, Elsevier, vol. 9(4), pages 369-383.
    2. John F. Muth, 1986. "Search Theory and the Manufacturing Progress Function," Management Science, INFORMS, INFORMS, vol. 32(8), pages 948-962, August.
    3. Paul S. Adler & Kim B. Clark, 1991. "Behind the Learning Curve: A Sketch of the Learning Process," Management Science, INFORMS, INFORMS, vol. 37(3), pages 267-281, March.
    4. Towill, Denis R., 1990. "Forecasting learning curves," International Journal of Forecasting, Elsevier, Elsevier, vol. 6(1), pages 25-38.
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    Cited by:
    1. Sawik, Tadeusz, 2010. "An integer programming approach to scheduling in a contaminated area," Omega, Elsevier, Elsevier, vol. 38(3-4), pages 179-191, June.
    2. Janiak, Adam & Rudek, RadosLaw, 2010. "A note on a makespan minimization problem with a multi-ability learning effect," Omega, Elsevier, Elsevier, vol. 38(3-4), pages 213-217, June.
    3. Tarakci, Hakan & Tang, Kwei & Teyarachakul, Sunantha, 2009. "Learning effects on maintenance outsourcing," European Journal of Operational Research, Elsevier, Elsevier, vol. 192(1), pages 138-150, January.
    4. Lee, Wen-Chiung & Wu, Chin-Chia & Hsu, Peng-Hsiang, 2010. "A single-machine learning effect scheduling problem with release times," Omega, Elsevier, Elsevier, vol. 38(1-2), pages 3-11, February.
    5. Sáenz-Royo, Carlos & Salas-Fumás, Vicente, 2013. "Learning to learn and productivity growth: Evidence from a new car-assembly plant," Omega, Elsevier, Elsevier, vol. 41(2), pages 336-344.

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