Monitoring Two Dependent Process Steps Using Special Variable Sample Sizes and Sampling Intervals Cause-Selecting Control Charts

Cause-selecting control charts are effective statistical process control tools for monitoring multistage processes. In this article, an adaptive statistical process control scheme to monitor a process with two dependent steps is proposed. Two different policies based on a combination of two different sample sizes and sampling intervals are utilized. Adjusted average time to signal measure, calculated through Markov chain approach, is applied to evaluate performance of the proposed control scheme. Numerical results indicate that the proposed scheme has improved performance over the fixed sample sizes at fixed sampling intervals scheme. Finally, the optimal parameters of the proposed scheme with two different policies are recommended, and comparisons between the minimum adjusted average time to signal of the proposed charts and variable sample sizes and sampling intervals cause-selecting control charts with three different sample sizes and sampling intervals are performed. It is shown that performance of the proposed scheme with four variable parameters is similar and even somewhat better than that of the scheme with six variable parameters. Copyright (c) 2011 John Wiley & Sons, Ltd.