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A review on coarse warranty data and analysis

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  • Wu, Shaomin

Abstract

Warranty data contain useful information about product quality and reliability, but they are usually coarse data because they may be aggregated, delayed, censored, missing or vague. They might, however, be the only forms of warranty data a manufacturer has, analysing such data are therefore needed and can also be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy, and forecasting future warranty claims needed for preparing warranty reserves plans.

Suggested Citation

  • Wu, Shaomin, 2013. "A review on coarse warranty data and analysis," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 1-11.
    • Handle: RePEc:eee:reensy:v:114:y:2013:i:c:p:1-11
    DOI: 10.1016/j.ress.2012.12.021
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhao, Xiujie & He, Shuguang & Xie, Min, 2018. "Utilizing experimental degradation data for warranty cost optimization under imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 108-119.
    2. Wu, Shaomin, 2014. "Construction of asymmetric copulas and its application in two-dimensional reliability modelling," European Journal of Operational Research, Elsevier, vol. 238(2), pages 476-485.
    3. Zhou, Chongwen & Chinnam, Ratna Babu & Dalkiran, Evrim & Korostelev, Alexander, 2017. "Bayesian approach to hazard rate models for early detection of warranty and reliability problems using upstream supply chain information," International Journal of Production Economics, Elsevier, vol. 193(C), pages 316-331.
    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. Xiaolin Wang & Wei Xie, 2018. "Two-dimensional warranty: A literature review," Journal of Risk and Reliability, , vol. 232(3), pages 284-307, June.
    6. Khan, Samir & Phillips, Paul & Jennions, Ian & Hockley, Chris, 2014. "No Fault Found events in maintenance engineering Part 1: Current trends, implications and organizational practices," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 183-195.
    7. Utkin, Lev V. & Coolen, Frank P.A. & Gurov, Sergey V., 2015. "Imprecise inference for warranty contract analysis," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 31-39.
    8. Wu, Shaomin, 2014. "Warranty return policies for products with unknown claim causes and their optimisation," International Journal of Production Economics, Elsevier, vol. 156(C), pages 52-61.
    9. Nikunja Mohan Modak & Shibaji Panda & Shib Sankar Sana, 2015. "Managing a two-echelon supply chain with price, warranty and quality dependent demand," Cogent Business & Management, Taylor & Francis Journals, vol. 2(1), pages 1011014-101, December.
    10. 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.
    11. Shokouhyar, Sajjad & Ahmadi, Sadra & Ashrafzadeh, Mahdi, 2021. "Promoting a novel method for warranty claim prediction based on social network data," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Wang, Xiaolin & Li, Lishuai & Xie, Min, 2020. "An unpunctual preventive maintenance policy under two-dimensional warranty," European Journal of Operational Research, Elsevier, vol. 282(1), pages 304-318.
    13. 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.
    14. Jeon, Jeasu & Sohn, So Young, 2015. "Product failure pattern analysis from warranty data using association rule and Weibull regression analysis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 176-183.

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