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Early detection of reliability related problems from two-dimensional warranty data considering labour code priority index

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  • Gupta, Sanjib Kumar
  • Chattopadhyay, Gaurangadeb

Abstract

Early detection of reliability related problems through the use of sensitive statistical methods, allowing early actions to mitigate potential reliability problems could save a considerable amount of money and product goodwill for large manufacturing companies. The items which are covered under two-dimensional warranty, both age and usage are important and apart from the number of failures, the cost of repair needs to be considered to adequately tackle the problem. In the present paper, we have addressed the above issues and developed an index, known as Labour Code Priority Index (LCPI), based on number of failure at different age-usage windows as well as the cost of repair of the failed items. We have explored various desirable and interesting properties of the index and borrowed the strength of the CUSUM technique applied on the sequentially generated index values to identify early reliability related issues both for a simulated as well as, a real-life synthetic data.

Suggested Citation

  • Gupta, Sanjib Kumar & Chattopadhyay, Gaurangadeb, 2022. "Early detection of reliability related problems from two-dimensional warranty data considering labour code priority index," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002320
    DOI: 10.1016/j.ress.2022.108588
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    References listed on IDEAS

    as
    1. Yang, Duo & He, Zhen & He, Shuguang, 2016. "Warranty claims forecasting based on a general imperfect repair model considering usage rate," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 147-154.
    2. Altun, Mustafa & Comert, Salih Vehbi, 2016. "A change-point based reliability prediction model using field return data," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 175-184.
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    4. Lin, Kunsong & Chen, Yunxia, 2021. "Analysis of two-dimensional warranty data considering global and local dependence of heterogeneous marginals," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Chehade, Abdallah & Savargaonkar, Mayuresh & Krivtsov, Vasiliy, 2022. "Conditional Gaussian mixture model for warranty claims forecasting," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    6. Shakhatreh, Mohammed K. & Lemonte, Artur J. & Moreno–Arenas, Germán, 2019. "The log-normal modified Weibull distribution and its reliability implications," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 6-22.
    7. Kumar Gupta, Sanjib & De, Soumen & Chatterjee, Aditya, 2017. "Some reliability issues for incomplete two-dimensional warranty claims data," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 64-77.
    8. Sabri-Laghaie, Kamyar & Fathi, Mahdi & Zio, Enrico & Mazhar, Maryam, 2022. "A novel reliability monitoring scheme based on the monitoring of manufacturing quality error rates," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    9. Chenglong Li & Xiaolin Wang & Lishuai Li & Min Xie & Xin Wang, 2020. "On dynamically monitoring aggregate warranty claims for early detection of reliability problems," IISE Transactions, Taylor & Francis Journals, vol. 52(5), pages 568-587, May.
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    11. Gupta, Sanjib Kumar & De, Soumen & Chatterjee, Aditya, 2014. "Warranty forecasting from incomplete two-dimensional warranty data," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 1-13.
    12. Gupta, Sanjib Kumar & Bhattacharya, Debasis, 2022. "Non-parametric estimation of bivariate reliability from incomplete two-dimensional warranty data," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    13. David Stephens & Martin Crowder, 2004. "Bayesian analysis of discrete time warranty data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 195-217, January.
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