Univariate (X)over-bar control charts for individual characteristics in a multinormal model

The early work on multivariate statistical process control was built upon H otelling's T-2 control chart which was developed to simultaneously monitor the means of correlated quality variables. This chart, however, has a drawb ack, namely, the problem of identifying the responsible variable(s) when an out-of-control signal occurs. One alternative is to use a separate Xmacr c ontrol chart for each individual characteristic with equal risks, based on Bonferroni inequality. In this study, we show that, from an economic perspe ctive, it may be desirable to have unequal type I risks for the individual charts, because of different inspection and restoration costs associated wi th each variable. We obtain their risk ratios, which are measures of relati ve importance of the variables monitored. Then, based on these risk ratios, we develop computer algorithms for finding the exact control limits for in dividual variables from a multinormal distribution, in the sense that the o verall type I risk of the charts is equal to the desired value. Numerical s tudies show that the proposed methods give optimal or near-optimal results from an economic as well as statistical point of view.