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Impact of quality variations on product reliability

Author

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  • Jiang, R.
  • Murthy, D.N.P.

Abstract

The reliability of manufactured products can differ from the desired design reliability due to variations in manufacturing quality. Failure data from field provide useful information to assess if the changes in product reliability variations are significant or not and to identify the cause for the variation. In order to devise statistical methods to detect this, one needs to model the effect of quality variations in manufacturing on product reliability. This paper looks at this issue and examines the Weibull case in detail.

Suggested Citation

  • Jiang, R. & Murthy, D.N.P., 2009. "Impact of quality variations on product reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 490-496.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:2:p:490-496
    DOI: 10.1016/j.ress.2008.05.009
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    References listed on IDEAS

    as
    1. Murthy, D. N. P. & Djamaludin, I., 2002. "New product warranty: A literature review," International Journal of Production Economics, Elsevier, vol. 79(3), pages 231-260, October.
    2. Djamaludin, I. & Murthy, D. N. P. & Wilson, R. J., 1994. "Quality control through lot sizing for items sold with warranty," International Journal of Production Economics, Elsevier, vol. 33(1-3), pages 97-107, January.
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    Citations

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    Cited by:

    1. Murthy, D.N.P. & Hagmark, P.-E. & Virtanen, S., 2009. "Product variety and reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1601-1608.
    2. Edwards, David J. & Guess, Frank M. & León, Ramón V. & Young, Timothy M. & Crookston, Kevin A., 2014. "Improving estimates of critical lower percentiles by induced censoring," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 47-56.
    3. Zhang, Tian & Homri, Lazhar & Dantan, Jean-Yves & Siadat, Ali, 2023. "Models for reliability assessment of reconfigurable manufacturing system regarding configuration orders," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. M Bebbington & C D Lai & D N P Murthy & R Zitikis, 2009. "Modelling N- and W-shaped hazard rate functions without mixing distributions," Journal of Risk and Reliability, , vol. 223(1), pages 59-69, March.
    5. Crookston, Kevin A. & Mark Young, Timothy & Harper, David & Guess, Frank M., 2011. "Statistical reliability analyses of two wood plastic composite extrusion processes," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 172-177.
    6. 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).
    7. Lai, Chin-Diew & Izadi, Muhyiddin, 2012. "Generalized logistic frailty model," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 1969-1977.
    8. Zhi-Sheng Ye & Loon-Ching Tang & Min Xie, 2014. "Bi-objective burn-in modeling and optimization," Annals of Operations Research, Springer, vol. 212(1), pages 201-214, January.
    9. Li, Mingyang & Meng, Hongdao & Zhang, Qingpeng, 2017. "A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 95-104.
    10. Yihai He & Linbo Wang & Zhenzhen He & Xun Xiao, 2016. "Modelling infant failure rate of electromechanical products with multilayered quality variations from manufacturing process," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6594-6612, November.

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