Bayesian modified group chain sampling plan for binomial distribution using beta prior through quality region Online publication date: Mon, 15-Aug-2022
by Waqar Hafeez; Nazrina Aziz
International Journal of Productivity and Quality Management (IJPQM), Vol. 36, No. 4, 2022
Abstract: A useful technique in statistical quality assurance is acceptance sampling. It is used to take decision about the lot, either accepted or rejected, based on inspection of a random sample from the lot. Experts say that Bayesian approach is the best approach to make a correct decision when historical knowledge is available. Based on Bayesian approach, this study develops a Bayesian modified group chain sampling plan (BMGChSP) by using binomial distribution with beta prior. Two quality regions are found namely: probabilistic quality region (PQR) and indifference quality region (IQR). Acceptable quality level (AQL) based on producer's risk and limiting quality level (LQL) based on consumer's risk are used to select design parameters for BMGChSP. The values based on all possible combinations of design parameters for BMGChSP are tabulated and inflection points are found. The results expose that proposed plan is a good alternative of the existing traditional group chain sampling plans.
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