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Learning curve analysis in total productive maintenance

  • Wang, F. -K.
  • Lee, W.
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    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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    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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    1. Towill, Denis R., 1990. "Forecasting learning curves," International Journal of Forecasting, Elsevier, vol. 6(1), pages 25-38.
    2. Paul S. Adler & Kim B. Clark, 1991. "Behind the Learning Curve: A Sketch of the Learning Process," Management Science, INFORMS, vol. 37(3), pages 267-281, March.
    3. John F. Muth, 1986. "Search Theory and the Manufacturing Progress Function," Management Science, INFORMS, vol. 32(8), pages 948-962, August.
    4. Towill, Denis R., 1985. "Management systems applications of learning curves and progress functions," Engineering Costs and Production Economics, Elsevier, vol. 9(4), pages 369-383.
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