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Modeling lean maintenance metric using incidence matrix approach

Author

Listed:
  • Shashank Gupta

    (LULEÅ University of Technology)

  • Piyush Gupta

    (Inter-University Accelerator Centre)

  • Aditya Parida

    (LULEÅ University of Technology)

Abstract

Lean Maintenance (LM) enhances organizational profitability by identifying and eliminating maintenance related wastes. There exists no singular metric that measures maintenance related wastes. The paper identifies the LM features and models them using incidence-matrix. LM features are represented by diagonal elements, while its off-diagonal elements represent mutual influences among the LM features. The maintenance system leanness is quantified using the permanent of the matrix. The metric of leanness is proposed to be defined as Lean maintenance index (LMI) and is a ratio of the actual to the ideal values of permanent of actual and ideal maintenance system matrices. A high value of LMI indicates that the maintenance system is operating in a reduced waste scenario with respect to its resources. Among all the LM features, LMI was found to be most sensitive to management support including organizational processes. The results of the methodology are a good guide for managers. The shortcoming of the methodology is that, it relies on values and weights of the inter-relations among the features, which may not be necessarily true and may need further scientific rigor. The proposed methodology of using LMI as a singular metric to judge maintenance efficacy is expected to aid the operation managers in quantifying the maintenance leanness and may help them focus their efforts appropriately. There is no evidence to indicate existence of comprehensive list of LM features that culminate into a singular metric of maintenance productivity. This paper attempts to fill this gap.

Suggested Citation

  • Shashank Gupta & Piyush Gupta & Aditya Parida, 2017. "Modeling lean maintenance metric using incidence matrix approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(4), pages 799-816, December.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:4:d:10.1007_s13198-017-0671-z
    DOI: 10.1007/s13198-017-0671-z
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    References listed on IDEAS

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    Cited by:

    1. Deepak Sinwar & Monika Saini & Dilbag Singh & Drishty Goyal & Ashish Kumar, 2021. "Availability and performance optimization of physical processing unit in sewage treatment plant using genetic algorithm and particle swarm optimization," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1235-1246, December.

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