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Sequential Defect Removal Sampling

Author

Listed:
  • Douglas G. Bonett

    (College of Business, University of Wyoming, Department of Management and Marketing, P.O. Box 3275, Room 228, Laramie, Wyoming 82071-3275)

  • J. Arthur Woodward

    (University of California, Los Angeles, California 90024)

Abstract

Standard inspection methods underestimate the true number of defects or nonconformities in a complex product (e.g., automobile, mobile home, airplane, circuit board, computer program) when an inspector is unable to identify every defect with certainty. A nonlinear statistical model with a nonlinear constraint is developed for estimating the unknown number of defects in a product when inspection is imperfect. A sequential defect removal sampling plan is defined in which two or more inspectors examine in sequence a product or sample of products and then mark or correct any observed defects prior to the next inspection. The number of defects identified by each inspector provides the information needed to estimate the number of defects in the product in addition to the number of defects that have eluded all inspectors. A goodness-of-fit test of model assumptions is presented. A test of hypothesis regarding the unknown number of defects in quality improvement experiments also is described.

Suggested Citation

  • Douglas G. Bonett & J. Arthur Woodward, 1994. "Sequential Defect Removal Sampling," Management Science, INFORMS, vol. 40(7), pages 898-902, July.
    • Handle: RePEc:inm:ormnsc:v:40:y:1994:i:7:p:898-902
    DOI: 10.1287/mnsc.40.7.898
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    Citations

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    Cited by:

    1. Chun, Young H., 2012. "Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing," European Journal of Operational Research, Elsevier, vol. 217(3), pages 673-678.
    2. Young H. Chun, 2008. "Bayesian Analysis of the Sequential Inspection Plan via the Gibbs Sampler," Operations Research, INFORMS, vol. 56(1), pages 235-246, February.
    3. Chun, Young H., 2016. "Designing repetitive screening procedures with imperfect inspections: An empirical Bayes approach," European Journal of Operational Research, Elsevier, vol. 253(3), pages 639-647.
    4. Chun, Young H. & Sumichrast, Robert T., 2007. "Bayesian inspection model with the negative binomial prior in the presence of inspection errors," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1188-1202, November.
    5. Gong, Linguo, 2012. "The effect of testing errors on a repetitive testing process," European Journal of Operational Research, Elsevier, vol. 220(1), pages 115-124.

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