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RESEARCH PRODUCT

Stochastic Decision Support Models and Optimal Stopping Rules in a New Product Lifetime Testing

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description

Determining when to stop a statistical test is an important management decision. Several stopping criteria have been proposed, including criteria based on statistical similarity, the probability that the system has a desired reliability, and the expected cost of remaining faults. This paper presents a new stopping rule in fixed-sample testing based on the statistical estimation of total costs involved in the decision to continue beyond an early failure as well as a stopping rule in sequential-sample testing to determine when testing should be stopped. The paper considers the problem that can be stated as follows. A new product is submitted for lifetime testing. The product will be accepted if a random sample of n items shows less than s failures in performance testing. We want to know whether to stop the test before it is completed if the results of the early observations are unfavorable. A suitable stopping decision saves the cost of the waiting time for completion. On the other hand, an incorrect stopping decision causes an unnecessary design change and a complete rerun of the test. It