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Attribute Sampling Plan for Submitted Lots Based on Prior Information and Bayesian Approach

Author

Listed:
  • Jing Zhao

    (China National Institute of Standardization, Beijing 100191, China)

  • Fengyun Zhang

    (School of Mathematics and Statistics, Beijing Technology and Business University, Beijing 102488, China)

  • Xuan Zhang

    (China National Institute of Standardization, Beijing 100191, China)

  • Yuping Hu

    (School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, China)

  • Wenxing Ding

    (China National Institute of Standardization, Beijing 100191, China)

Abstract

An acceptance sampling plan is a method used to make a decision about acceptance or rejection of a product based on adherence to a standard. Meanwhile, prior information, such as the process capability index (PCI), has been applied in different manufacturing industries to improve the quality of manufacturing processes and the quality inspection of products. In this paper, an attribute sampling plan is developed for submitted lots based on prior information and Bayesian approach. The new attribute sampling plans adjust sample sizes to prior information based on the status of the inspection target. To be specific, the sampling plans in this paper are indexed by the parameter trust with levels of low, medium, and high, where increasing trust level reduces sample size or risk. PCIs are an important basis for the choice of the trust level. In addition, multiple comparisons have been performed, including producer’s risk and consumer’s risk under different prior information parameters and different sample sizes.

Suggested Citation

  • Jing Zhao & Fengyun Zhang & Xuan Zhang & Yuping Hu & Wenxing Ding, 2024. "Attribute Sampling Plan for Submitted Lots Based on Prior Information and Bayesian Approach," Mathematics, MDPI, vol. 12(11), pages 1-13, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1692-:d:1404788
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    References listed on IDEAS

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    1. Godfrey, Jt & Andrews, Rw, 1982. "A Finite Population Bayesian Model For Compliance Testing," Journal of Accounting Research, Wiley Blackwell, vol. 20(2), pages 304-315.
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