This article focuses on the estimation of dispersion effects in off‐line quality control techniques. In this context, the Taguchi design for the optimal choice of process parameters is one of the most commonly used statistical methods. Starting from Taguchi methodology, we consider that an additive or a multiplicative model defines the relationship between the deterministic component and the variability of the process. We apply a hypothesis testing in order to find the optimal factor combination that minimizes the variability indicator of product quality, using ranking and selection methods of the Bechhofer kind. Moreover, an extensive simulation study shows how the probability of finding the optimal set of factors changes according to the main sampling parameters, in order to provide guidance for practitioners.

The availability of information that suppliers possess about the production process, as well as about the technical and economic consequences for customers, encourages the development and application of acceptance sampling plans that follow economic criteria, such as the Bayesian ones proposed in the literature. The combination of prior knowledge described by the prior distribution and empirical knowledge based on the sample leads to the decision to accept or reject the lot under inspection. The main purpose of this study was to derive acceptance sampling plans for attributes based on a prior generalized beta distribution following the economic criterion to minimize the expected total cost of quality. Specifically, a procedure is proposed to define the optimal sampling plan based on the technical characteristics of the production process and the costs inherent in the quality of the product. After the methodological aspects are described in detail, an extensive simulation study is reported that demonstrates how the optimal plan changes according to the main parameters, providing guidance for practitioners.

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