Cost-Optimal Multistage Sampling Plans in Statistical Quality Control
Multistage Bayesian decision procedures in statistical quality control are known from attribute sampling. In this paper they are introduced in a more general framework occuring in lot-control by using the theory of Bayesian sequentially planned decision procedures. We show that under sufficiency and transitivity assumptions and monotonicity properties concerning the distributionand cost set-up these Bayes-procedures have(z,c-,c+)-structure which, on one hand, generalizes results of K.-H. Waldmann and, on the other hand, reduces computational effort significantly. Finally, examples taken from attribute sampling and life testing for an outgoing lot are presented.
Acceptance sampling, backward induction, Bayes procedures, multistage decision procedures, quality control