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Modelling integrated inventory, maintenance and nonconforming output management of imperfect deteriorating production systems

Author

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  • Vlastos, Spyros I.
  • Ioannidis, Stratos
  • Koulouriotis, Dimitrios E.

Abstract

The advancement of knowledge concerning optimal practices in production systems management necessitates the continual development of comprehensive models, approximating real-world conditions. This study aims to contribute towards modelling and analysis of the decision parameters that govern the management of gradually deteriorating imperfect production systems. The integration of demand parameters represents an intriguing avenue for investigation, offering a comparative perspective on the efficacy of these parameters on nonconforming outcome management. Operating production equipment is subject to gradual deterioration, ultimately leading to complete failure. A condition-based maintenance policy, based on inspections, governed by a specific maintenance threshold correlated with the deterioration level, is employed. Depending on its extent, deterioration negatively affects the percentage of nonconforming parts produced. Moreover, nonconforming parts management policies, such as merchandising under varying demand conditions or reprocessing of such parts are investigated. Systems operation modelling is facilitated through continuous-time Markov chains. Execution of numerical experiments reveals the behavior of performance metrics while alternating system parameters. Concurrently, these experiments demonstrate the impact of implementing a specific management policy. It is pointed out that unilaterally optimizing a single key performance indicator may not always enhance the overall result.

Suggested Citation

  • Vlastos, Spyros I. & Ioannidis, Stratos & Koulouriotis, Dimitrios E., 2025. "Modelling integrated inventory, maintenance and nonconforming output management of imperfect deteriorating production systems," Reliability Engineering and System Safety, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:reensy:v:263:y:2025:i:c:s0951832025004740
    DOI: 10.1016/j.ress.2025.111273
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