Designing Bayesian new two‐sided group chain sampling plan for quality regions based on beta prior

Abstract

AbstractAcceptance sampling is used to determine whether to accept or reject the lot that is being inspected. The Bayesian strategy is the ideal method to use when historical data about the product is available in order to make decision. To estimate quality regions, we suggest a Bayesian new two‐sided group chain sampling plan in this study. The operating characteristic function is developed using the binomial distribution, and average probability of acceptance is estimated using beta distribution as prior. Four quality regions are estimated for certain predetermined values of producer's and consumer's risks. Producer's risk and consumer's risk are correlated with acceptable quality level and limiting quality level of quality regions, respectively. For the proposed plan, inflection point values are estimated based on various combinations of design parameters. Operating characteristic curves are use up to compare the current Bayesian group chain sampling plan (BGChSP) with the effects of changing the values of specified parameter values. The suggested plan is a well replacement for current industry plans, according to the operating characteristic curve's findings.