Abstract
Abstract This paper describes the basic concepts and philosophies of an improved approach to attribute acceptance sampling, which has been tested successfully since 1971. The main measures of performance are expected outgoing quality and expected quality assurance cost. Boundary curves help portray feasibilities of quality-cost combinations, which leads to selections among Pareto optimal sampling plans. Quality and Cost effects are quantified by few parameters which represent situations such as: destructive or nondestructive sampling; effectiveness in finding and correcting defects; reworking vs. scrapping, etc. The model is structured as a Markov Decision Problem, with transition probabilities between sampling stages given by Bayesian estimates, with optimal solutions obtained through Dynamic Programming, with constraints to represent contractual restrictions, and with directed steps to evaluate given sampling plans. Practical applications require computer support.
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