A Bayesian Analysis of the Deming Cost Model with Normally Distributed Sampling Data

ISSN： 0898-2112

Source： Quality Engineering, Vol.18, Iss.2, 2006-07, pp. : 107-116

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Abstract

This article studies the Deming cost model using a Bayesian approach when the quality characteristic of items is assumed to have a normal distribution with unknown mean. Previously, researchers studied this model by the go/no-go data. Through a Bayesian approach, the model consists of a two-stage decision that minimizes the expected total cost: The first stage decision is to determine the optimal sample size, and the second stage decision is to decide whether to stop inspection or continue to inspect the remaining items of the lot. Numerical integration is used to find an approximate solution to the model. An illustrative example is given and a numerical analysis of this example is performed to realize the effects of the model parameters. The cost difference between using the measurement data and the corresponding go/no-go data under the same probability assumptions and cost structure is also investigated.