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Critical spare parts ordering decisions using conditional reliability and stochastic lead time

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  • Godoy, David R.
  • Pascual, Rodrigo
  • Knights, Peter

Abstract

Asset-intensive companies face great pressure to reduce operation costs and increase utilization. This scenario often leads to over-stress on critical equipment and its spare parts associated, affecting availability, reliability, and system performance. As these resources impact considerably on financial and operational structures, the opportunity is given by demand for decision-making methods for the management of spare parts processes. We proposed an ordering decision-aid technique which uses a measurement of spare performance, based on the stress–strength interference theory; which we have called Condition-Based Service Level (CBSL). We focus on Condition Managed Critical Spares (CMS), namely, spares which are expensive, highly reliable, with higher lead times, and are not available in store. As a mitigation measure, CMS are under condition monitoring. The aim of the paper is orienting the decision time for CMS ordering or just continuing the operation. The paper presents a graphic technique which considers a rule for decision based on both condition-based reliability function and a stochastic/fixed lead time. For the stochastic lead time case, results show that technique is effective to determine the time when the system operation is reliable and can withstand the lead time variability, satisfying a desired service level. Additionally, for the constant lead time case, the technique helps to define insurance spares. In conclusion, presented ordering decision rule is useful to asset managers for enhancing the operational continuity affected by spare parts.

Suggested Citation

  • Godoy, David R. & Pascual, Rodrigo & Knights, Peter, 2013. "Critical spare parts ordering decisions using conditional reliability and stochastic lead time," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 199-206.
    • Handle: RePEc:eee:reensy:v:119:y:2013:i:c:p:199-206
    DOI: 10.1016/j.ress.2013.05.026
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    References listed on IDEAS

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    Cited by:

    1. Poppe, Joeri & Basten, Rob J.I. & Boute, Robert N. & Lambrecht, Marc R., 2017. "Numerical study of inventory management under various maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 262-273.
    2. Godoy, David R. & Pascual, Rodrigo & Knights, Peter, 2014. "A decision-making framework to integrate maintenance contract conditions with critical spares management," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 102-108.
    3. Mizutani, Daijiro & Nakazato, Yuto & Ikushima, Rie & Satsukawa, Koki & Kawasaki, Yosuke & Kuwahara, Masao, 2024. "Optimal intervention policy of emergency storage batteries for expressway transportation systems considering deterioration risk during lead time of replacement," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Sleptchenko, Andrei & van der Heijden, Matthieu, 2016. "Joint optimization of redundancy level and spare part inventories," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 64-74.
    5. Yongquan, Sun & Xi, Chen & He, Ren & Yingchao, Jin & Quanwu, Liu, 2016. "Ordering decision-making methods on spare parts for a new aircraft fleet based on a two-sample prediction," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 40-50.
    6. Zhu, Sha & van Jaarsveld, Willem & Dekker, Rommert, 2022. "Critical project planning and spare parts inventory management in shutdown maintenance," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Ramin Moghaddass & Şeyda Ertekin, 2018. "Joint optimization of ordering and maintenance with condition monitoring data," Annals of Operations Research, Springer, vol. 263(1), pages 271-310, April.
    8. Dong Lyu & Shubin Si & Zhiqiang Cai & Liyang Xie, 2020. "Computational method for importance measure of the k-out-of-n system based on stress–strength interference," Journal of Risk and Reliability, , vol. 234(1), pages 27-40, February.

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