Selecting the Best Manufacturing Process

Abstract

This paper deals with the problem of selecting the best manufacturing process (the one that has the largest process capability ratio index) from among k available manufacturing processes. When the product quality characteristic data of each process follows a normal distribution, a modified likelihood ratio (MLR) selection rule is proposed. The sample size and the critical value, which are called for by this MLR rule, are computed by controlling the error probability and the probability of a correct selection. When the product quality characteristic data of each process follows a non-normal symmetric distribution, a simulation study, using the IMSL software, is used to study the robustness of this selection rule. The results suggest that the proposed rule is insensitive to certain symmetric non-normal (such as uniform and logistic) process distributions.

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