Abstract

Bayesian Acceptance Sampling Approach is associated with utilization of prior process history for the selection of Distributions (viz., Gamma Poisson, Beta Binomial) to describe the random fluctuations involved in Acceptance Sampling. The Acceptance sampling procedures are the practical tools for quality assurance applications involving product control. Acceptance sampling systems are advocated when small sample size are necessary or desirable towards costlier testing for product quality. The sampling plan which provides the vendor and buyer decision rules for product acceptance to meet the preset product quality requirement.

The main thrust of this paper is to account for the possibility of dependence among the items of a sample. The development of new method for designing sampling plans based on range of quality instead of point- wise description of quality by invoking a novel approach called Quality Regions. This method seems to be versatile and can be adopted to the elementary production process where the stipulated quality level is advisable to fix at a later stage and provides a new procedure meant for designing Bayesian Chain Sampling Plan involving Quality Regions. Maximum Allowable Percent Defective (MAPD) is also considered for the selection of parameters for Bayesian Chain Sampling Plan. New quality descriptors called operating ratios are introduced to design the sampling plan.

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