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A new policy for designing acceptance sampling plan based on Bayesian inference in the presence of inspection errors

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  • Mohammad Saber Fallah Nezhad
  • Faeze Zahmatkesh Saredorahi

Abstract

The purpose of this article is to present a new policy for designing an acceptance sampling plan based on the minimum proportion of the lot that should be inspected in the presence of inspection errors. It is assumed that inspection is not perfect and every defective item cannot be detected with complete certainty. The Bayesian method is used for obtaining the probability distribution function of the number of defective items in the lot. To design this model, two constraints of producer risk and consumer risk are considered during the inspection process by using two specified points on operating characteristic curve. In order to illustrate the application of the proposed model, an example is presented. In addition, a sensitivity analysis is performed to analyze the model performance under different scenarios of process parameters and the results are elaborated. Finally, the efficiency of the proposed model is compared with the sampling method of Spencer and Kevan de Lopez (2017) at the same conditions.

Suggested Citation

  • Mohammad Saber Fallah Nezhad & Faeze Zahmatkesh Saredorahi, 2018. "A new policy for designing acceptance sampling plan based on Bayesian inference in the presence of inspection errors," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(21), pages 5307-5318, November.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:21:p:5307-5318
    DOI: 10.1080/03610926.2017.1390130
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    Cited by:

    1. Pérez-González, Carlos J. & Fernández, Arturo J. & Kohansal, Akram, 2020. "Efficient truncated repetitive lot inspection using Poisson defect counts and prior information," European Journal of Operational Research, Elsevier, vol. 287(3), pages 964-974.

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