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Learning curve analysis in total productive maintenance

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  • Wang, F. -K.
  • Lee, W.

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.

Suggested Citation

  • Wang, F. -K. & Lee, W., 2001. "Learning curve analysis in total productive maintenance," Omega, Elsevier, vol. 29(6), pages 491-499, December.
    • Handle: RePEc:eee:jomega:v:29:y:2001:i:6:p:491-499
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    References listed on IDEAS

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    Cited by:

    1. Li, Dong & Nagurney, Anna & Yu, Min, 2018. "Consumer learning of product quality with time delay: Insights from spatial price equilibrium models with differentiated products," Omega, Elsevier, vol. 81(C), pages 150-168.
    2. Chun Su & Longfei Cheng, 2018. "An availability-based warranty policy considering preventive maintenance and learning effects," Journal of Risk and Reliability, , vol. 232(6), pages 576-586, December.
    3. Tarakci, Hakan & Tang, Kwei & Teyarachakul, Sunantha, 2009. "Learning effects on maintenance outsourcing," European Journal of Operational Research, Elsevier, vol. 192(1), pages 138-150, January.
    4. Janiak, Adam & Rudek, RadosLaw, 2010. "A note on a makespan minimization problem with a multi-ability learning effect," Omega, Elsevier, vol. 38(3-4), pages 213-217, June.
    5. Sáenz-Royo, Carlos & Salas-Fumás, Vicente, 2013. "Learning to learn and productivity growth: Evidence from a new car-assembly plant," Omega, Elsevier, vol. 41(2), pages 336-344.
    6. Sawik, Tadeusz, 2010. "An integer programming approach to scheduling in a contaminated area," Omega, Elsevier, vol. 38(3-4), pages 179-191, June.
    7. Lee, Wen-Chiung & Wu, Chin-Chia & Hsu, Peng-Hsiang, 2010. "A single-machine learning effect scheduling problem with release times," Omega, Elsevier, vol. 38(1-2), pages 3-11, February.
    8. Hung, Yick-Hin & Li, Leon Y.O. & Cheng, T.C.E., 2022. "Uncovering hidden capacity in overall equipment effectiveness management," International Journal of Production Economics, Elsevier, vol. 248(C).

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