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Some New Advances in Modeling for Performance-Based Maintenance Services

In: Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis

Author

Listed:
  • Tongdan Jin

    (Ingram School of Engineering, Texas State University)

  • Yisha Xiang

    (Texas Tech University)

  • Jin Qin

    (School of Management, The University of Science and Technology of China)

  • Vinod Subramanyam

    (Ingram School of Engineering, Texas State University)

Abstract

Performance-based maintenance (PBM) has emerged as a new after-sales service contract to support the acquisition and operation of capital equipment. Under PBM contract, the service provider strives to achieve its performance goal while realizing its own business interests. In this chapter, we introduce several performance measures commonly used in PBM contracting and further investigate their interdependencies under different payment and reward schemes. We focus our efforts on service-oriented industry including integrated manufacturing firms and third-party logistics providers. We present new PBM contracting models with the goal of maximizing the service profit and minimizing the maintenance cost rate, respectively. Numerical examples from wind energy industry are used to demonstrate the application of proposed models. Two managerial insights are derived. First, zero spare parts inventory is achievable if reliability, maintenance, and repair logistics are appropriately coordinated; second, when detailed maintenance cost data are unavailable, the optimal policies aimed to maximize the system availability can be used as a good substitute for obtaining near maximum service profit.

Suggested Citation

  • Tongdan Jin & Yisha Xiang & Jin Qin & Vinod Subramanyam, 2022. "Some New Advances in Modeling for Performance-Based Maintenance Services," International Series in Operations Research & Management Science, in: Adiel Teixeira de Almeida & Love Ekenberg & Philip Scarf & Enrico Zio & Ming J. Zuo (ed.), Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis, pages 459-486, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-89647-8_22
    DOI: 10.1007/978-3-030-89647-8_22
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