Generalized variance|S|quality control chart is very powerful way to detect small shifts in the mean vector. The main purpose of this paper, presents an improved the generalized variance |S|quality control chart for multivariate process. Generalized variance chart allow us to simultaneously monitor whether joint variability of two or more related variables is in control. In addition, a control ...

Detecting dynamic mean shifts is particularly important in monitoring feedbackcontrolled processes in which time-varying shifts are usually observed. When multivariate control charts are being utilized, one way to improve performance is to reduce dimensions. However, it is difficult to identify and remove non-informative variables statically in a process with dynamic shifts, as the contribution...

Several characteristics, say d of them, are measured on the output of a process. Such characteristics may or may not be correlated. A case has been made that one should not monitor such processes by keeping separate control charts on each of the d characteristics. What is often suggested is a single control chart based on Hotelling’s T . See Alt (1985) and Murphy (1987) on this point and for fu...

Multivariate control charts are very effective when monitoring several correlated characteristics. Proper process control consists of monitoring both mean and variability. In multivariate settings, the multivariate exponentially weighted moving average (MEWMA) is ideal to monitor the mean vector. Similarly, the recently-proposed multivariate exponentially weighted moving covariance matrix (MEWM...