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Normal to extreme Operation Transition Threshold analysis for remanufacturing systems

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  • Zamanzad Gavidel, Saeed
  • Rickli, Jeremy L.

Abstract

Extremely large core returns can destabilize remanufacturing systems and is reported as a nuisance for remanufacturers. In this paper, extreme core returns are targeted by investigating the Operation Transition Threshold (OTT) criterion that signals a remanufacturing system's transition from normal operation mode to extreme operation mode. The threshold parameter of the Generalized Pareto Distribution (GPD) is used to signal this transition. The threshold of the GPD can be estimated by different statistical techniques that return different thresholds that can impact the performance of remanufacturing systems. To investigate the OTT of remanufacturing systems, candidate OTTs are first estimated by statistical techniques, then, using vote-counting, proper OTT(s) are identified. Finally, the efficiency of the statistical analysis and effectiveness of the estimated OTTs is validated by remanufacturing system technical requirements. A case study is presented that focuses on remanufacturing end-of-use industrial valves returning to valve shop of a chemical complex. The devised approach is applied to analyze the potential impacts of different OTT estimators on remanufacturing operations. Results of this work indicate that choosing improper OTTs can impose heavy costs on the remanufacturing system. Thus, the OTT can be a critical remanufacturing system parameter should extreme scenarios be a possibility.

Suggested Citation

  • Zamanzad Gavidel, Saeed & Rickli, Jeremy L., 2019. "Normal to extreme Operation Transition Threshold analysis for remanufacturing systems," International Journal of Production Economics, Elsevier, vol. 213(C), pages 46-54.
    • Handle: RePEc:eee:proeco:v:213:y:2019:i:c:p:46-54
    DOI: 10.1016/j.ijpe.2019.03.010
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    Cited by:

    1. Hosoda, Takamichi & Disney, Stephen M. & Zhou, Li, 2021. "The yield rate paradox in closed-loop supply chains," International Journal of Production Economics, Elsevier, vol. 239(C).

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