Opportunistic Maintenance Modeling in a Closed-Loop Supply Chain: The Impact of Spare Parts Material, Technician Skill, and Environmental Conditions

Authors

  • Mahnam Najafi * Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.
  • Hiva Faroughi Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.

https://doi.org/10.48313/scodm.v2i4.47

Abstract

Opportunistic Maintenance (OM) is one of the key approaches for optimizing maintenance operations in Closed-Loop Supply Chains (CLSC), which leverages the repair and recovery of spare parts to reduce costs and improve reliability. In this paper, a new OM model is proposed that, in addition to classical factors such as component age and economic and structural dependencies, incorporates spare-part material type, technician skill level, and environmental conditions into maintenance decision-making. The material of spare parts directly affects their failure rate and service life, while technician skill enhances repair quality and post-repair lifetime. Moreover, environmental conditions such as temperature and humidity alter the degradation rate of components. Preliminary results and conceptual analyses indicate that the proposed model can reduce costs, improve the overall performance of the closed-loop supply chain, and enhance the reliability of repaired components.

Keywords:

Opportunistic maintenance, Spare parts, Structural dependency, Closed-loop supply chain, Reliability

References

  1. [1] Jahangiri, S., Abolghasemian, M., Pourghader Chobar, A., Nadaffard, A., & Mottaghi, V. (2021). Ranking of key resources in the humanitarian supply chain in the emergency department of iranian hospital: A real case study in covid-19 conditions. Journal of applied research on industrial engineering, 8(Special Issue), 1–10. https://doi.org/10.22105/jarie.2021.275255.1263
  2. [2] Abolghasemian, M., Pourghader Chobar, A., Vaseei, M., & Nasiri Jan Agha, M. R. (2024). Mathematical modeling to evaluate knowledge management in the development of industrial clusters. International journal of applied operational research-an open access journal, 12(4), 1–18. http://dx.doi.org/10.71885/ijorlu-2024-1-677
  3. [3] Agrawal, V. V, Atasu, A., & Van Wassenhove, L. N. (2019). OM forum—new opportunities for operations management research in sustainability. Manufacturing & service operations management, 21(1), 1–12. https://doi.org/10.1287/msom.2017.0699
  4. [4] Kerin, M., & Pham, D. T. (2020). Smart remanufacturing: A review and research framework. Journal of manufacturing technology management, 31(6), 1205–1235. https://doi.org/10.1108/JMTM-06-2019-0205
  5. [5] Liu, C., Zhu, Q., Wei, F., Rao, W., Liu, J., Hu, J., & Cai, W. (2019). A review on remanufacturing assembly management and technology. The international journal of advanced manufacturing technology, 105(11), 4797–4808. https://doi.org/10.1007/s00170-019-04617-x%0A%0A
  6. [6] Selviaridis, K., & Wynstra, F. (2015). Performance-based contracting: a literature review and future research directions. International journal of production research, 53(12), 3505–3540. https://doi.org/10.1080/00207543.2014.978031
  7. [7] Souza, G. C. (2013). Closed-loop supply chains: A critical review, and future research. Decision sciences, 44(1), 7–38. https://doi.org/10.1111/j.1540-5915.2012.00394.x
  8. [8] Zhang, X., Li, Q., Liu, Z., & Chang, C. T. (2021). Optimal pricing and remanufacturing mode in a closed-loop supply chain of WEEE under government fund policy. Computers & industrial engineering, 151, 106951. https://doi.org/10.1016/j.cie.2020.106951
  9. [9] Ferraro, S., Baffa, F., Cantini, A., Leoni, L., De Carlo, F., & Campatelli, G. (2024). Exploring remanufacturing conveniency: An economic and energetic assessment for a closed-loop supply chain of a mechanical component. Journal of cleaner production, 458, 142504. https://doi.org/10.1016/j.jclepro.2024.142504
  10. [10] Turki, E., Jouini, O., Jemai, Z., Traiy, Y., Lazrak, A., Valot, P., & Heidseick, R. (2024). Forecasting spare part extractions from returned systems in a closed-loop supply chain. International journal of production research, 62(21), 7860–7876. https://doi.org/10.1080/00207543.2024.2333052
  11. [11] Daneshmand Mehr, M., & Abolghasemian, M. (2024). Multi objective uncertain mathematical programming for urban water supply system. Research annals of industrial and systems engineering, 1(4), 244–261. https://doi.org/10.22105/raise.vi.74
  12. [12] Ab-Samat, H., & Kamaruddin, S. (2014). Opportunistic maintenance (OM) as a new advancement in maintenance approaches: A review. Journal of quality in maintenance engineering, 20(2), 98–121. https://doi.org/10.1108/JQME-04-2013-0018
  13. [13] Keizer, M. C. A. O., Flapper, S. D. P., & Teunter, R. H. (2017). Condition-based maintenance policies for systems with multiple dependent components: A review. European journal of operational research, 261(2), 405–420. https://doi.org/10.1016/j.ejor.2017.02.044
  14. [14] Sun, J., Sun, Z., Chen, C., Yan, C., Jin, T., & Zhong, Y. (2023). Group maintenance strategy of CNC machine tools considering three kinds of maintenance dependence and its optimization. The international journal of advanced manufacturing technology, 124(11), 3749–3760. https://doi.org/10.1007/s00170-021-07752-6%0A%0A
  15. [15] Wang, J., Miao, Y., Yi, Y., & Huang, D. (2021). An imperfect age-based and condition-based opportunistic maintenance model for a two-unit series system. Computers & industrial engineering, 160, 107583. https://doi.org/10.1016/j.cie.2021.107583
  16. [16] Chateauneuf, A., Laggoune, R., & others. (2018). Condition based opportunistic preventive maintenance policy for utility systems with both economic and structural dependencies- application to a gas supply network. International journal of pressure vessels and piping, 165, 214–223. https://doi.org/10.1016/j.ijpvp.2018.07.001
  17. [17] Zhu, F., Hu, H., & Xu, F. (2022). Risk assessment model for international construction projects considering risk interdependence using the DEMATEL method. Plos one, 17(5), e0265972. https://doi.org/10.1371/journal.pone.0265972
  18. [18] Nguyen, H. S. H., Do, P., Vu, H. C., & Iung, B. (2019). Dynamic maintenance grouping and routing for geographically dispersed production systems. Reliability engineering & system safety, 185, 392–404. https://doi.org/10.1016/j.ress.2018.12.031
  19. [19] Lu, Y., Wang, S., Zhang, C., Chen, R., Dui, H., & Mu, R. (2024). Adaptive maintenance window-based opportunistic maintenance optimization considering operational reliability and cost. Reliability engineering & system safety, 250, 110292. https://doi.org/10.1016/j.ress.2024.110292
  20. [20] Dinh, D. H., Do, P., Iung, B., & Nguyen, P. T. N. (2024). Reliability modeling and opportunistic maintenance optimization for a multicomponent system with structural dependence. Reliability engineering & system safety, 241, 109708. https://doi.org/10.1016/j.ress.2023.109708
  21. [21] Xu, J., Liu, B., Zhao, X., & Wang, X. L. (2024). Online reinforcement learning for condition-based group maintenance using factored Markov decision processes. European journal of operational research, 315(1), 176–190. https://doi.org/10.1016/j.ejor.2023.11.039
  22. [22] Wang, J., Makis, V., & Zhao, X. (2019). Optimal condition-based and age-based opportunistic maintenance policy for a two-unit series system. Computers & industrial engineering, 134, 1–10. https://doi.org/10.1016/j.cie.2019.05.020
  23. [23] Salari, N., & Makis, V. (2020). Application of Markov renewal theory and semi-Markov decision processes in maintenance modeling and optimization of multi-unit systems. Naval research logistics (NRL), 67(7), 548–558. https://doi.org/10.1002/nav.21932
  24. [24] Xu, J., Liang, Z., Li, Y. F., & Wang, K. (2021). Generalized condition-based maintenance optimization for multi-component systems considering stochastic dependency and imperfect maintenance. Reliability engineering & system safety, 211, 107592. https://doi.org/10.1016/j.ress.2021.107592
  25. [25] Zhou, Y., Li, B., & Lin, T. R. (2022). Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning. Reliability engineering & system safety, 217, 108078. https://doi.org/10.1016/j.ress.2021.108078
  26. [26] Ahmad, H. H., Almetwally, E. M., & Ramadan, D. A. (2022). A comparative inference on reliability estimation for a multi-component stress-strength model under power Lomax distribution with applications. AIMS math, 7(10), 18050–18079. http://dx.doi.org/ 10.3934/math.2022994
  27. [27] Lin, S. W., Matanhire, T. B., & Liu, Y.-T. (2021). Copula-based bayesian reliability analysis of a product of a probability and a frequency model for parallel systems when components are dependent. Applied sciences, 11(4), 1697. https://doi.org/10.3390/app11041697
  28. [28] Boujarif, A., Coit, D. W., Jouini, O., Zeng, Z., & Heidsieck, R. (2025). Repairing smarter: Opportunistic maintenance for a closed-loop supply chain with spare parts dependency. Reliability engineering & system safety, 255, 110642. https://doi.org/10.1016/j.ress.2024.110642
  29. [29] Kim, S. H., Cohen, M. A., & Netessine, S. (2007). Performance contracting in after-sales service supply chains. Management science, 53(12), 1843–1858. https://doi.org/10.1287/mnsc.1070.0741
  30. [30] Jin, T., & Tian, Y. (2012). Optimizing reliability and service parts logistics for a time-varying installed base. European journal of operational research, 218(1), 152–162. https://doi.org/10.1016/j.ejor.2011.10.026
  31. [31] Der Kiureghian, A., & Liu, P.L. (1986). Structural reliability under incomplete probability information. Journal of engineering mechanics, 112(1), 85–104. https://doi.org/10.1061/(ASCE)0733-9399(1986)112:1(85)
  32. [32] Lebrun, R., & Dutfoy, A. (2009). An innovating analysis of the Nataf transformation from the copula viewpoint. Probabilistic engineering mechanics, 24(3), 312–320. https://doi.org/10.1016/j.probengmech.2008.08.001

Downloads

Published

2025-12-19

Issue

Vol. 2 No. 4 (2025): Supply Chain and Operations Decision Making

Section

Articles

How to Cite

Najafi, M. ., & Faroughi, H. . (2025). Opportunistic Maintenance Modeling in a Closed-Loop Supply Chain: The Impact of Spare Parts Material, Technician Skill, and Environmental Conditions. Supply Chain and Operations Decision Making, 2(4), 234-251. https://doi.org/10.48313/scodm.v2i4.47

Similar Articles

1-10 of 17

You may also start an advanced similarity search for this article.