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Optimising batch withdrawal strategies for non-safety-related defects under risk sensitivity.

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Title: Optimising batch withdrawal strategies for non-safety-related defects under risk sensitivity.
Authors: Fontem, Belleh1 (AUTHOR) belleh_fontem@uml.edu, Hellman, Kelly L.2 (AUTHOR)
Source: International Journal of Production Research. Jul2025, Vol. 63 Issue 13, p4639-4668. 30p.
Subjects: Maximum likelihood statistics, Product failure, Dynamic programming, Numerical analysis, Consumers
Abstract: A significant challenge in warranty-protected products is determining the appropriate strategy for removing batches that are vulnerable to non-safety-related failures. Two main approaches exist for handling such failures: an active strategy that compensates customers for current faulty units but preemptively withdraws remaining batch units to prevent future warranty claims, and a passive strategy that compensates customers throughout the warranty window without removing the batch. Under two different risk quantification methods, we examine the connection between a product's warranty configuration and the optimal timing strategy for withdrawing a batch that is susceptible to non-safety-related defects. The first method reveals a deterministic optimal withdrawal strategy, and we identify conditions where a passive strategy is optimal, irrespective of warranty length or batch size. In contrast, the optimal strategy under the second method is a probabilistic decision rule that depends on a random event's outcome. We also derive the maximum likelihood estimator for the parameters governing the product's failure probability, and use numerical analysis to explore the performance cost of dynamically learning these parameters. The results suggest that maximum likelihood estimation is a robust method for identifying the parameters of the failure probability function when these parameters are initially unknown during the warranty period. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Optimising batch withdrawal strategies for non-safety-related defects under risk sensitivity.
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  Data: <searchLink fieldCode="AR" term="%22Fontem%2C+Belleh%22">Fontem, Belleh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> belleh_fontem@uml.edu</i><br /><searchLink fieldCode="AR" term="%22Hellman%2C+Kelly+L%2E%22">Hellman, Kelly L.</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Jul2025, Vol. 63 Issue 13, p4639-4668. 30p.
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  Data: <searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Product+failure%22">Product failure</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Consumers%22">Consumers</searchLink>
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  Data: A significant challenge in warranty-protected products is determining the appropriate strategy for removing batches that are vulnerable to non-safety-related failures. Two main approaches exist for handling such failures: an active strategy that compensates customers for current faulty units but preemptively withdraws remaining batch units to prevent future warranty claims, and a passive strategy that compensates customers throughout the warranty window without removing the batch. Under two different risk quantification methods, we examine the connection between a product's warranty configuration and the optimal timing strategy for withdrawing a batch that is susceptible to non-safety-related defects. The first method reveals a deterministic optimal withdrawal strategy, and we identify conditions where a passive strategy is optimal, irrespective of warranty length or batch size. In contrast, the optimal strategy under the second method is a probabilistic decision rule that depends on a random event's outcome. We also derive the maximum likelihood estimator for the parameters governing the product's failure probability, and use numerical analysis to explore the performance cost of dynamically learning these parameters. The results suggest that maximum likelihood estimation is a robust method for identifying the parameters of the failure probability function when these parameters are initially unknown during the warranty period. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/00207543.2024.2441439
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      – Code: eng
        Text: English
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        PageCount: 30
        StartPage: 4639
    Subjects:
      – SubjectFull: Maximum likelihood statistics
        Type: general
      – SubjectFull: Product failure
        Type: general
      – SubjectFull: Dynamic programming
        Type: general
      – SubjectFull: Numerical analysis
        Type: general
      – SubjectFull: Consumers
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              Text: Jul2025
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              Y: 2025
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