A new phase II EWMA dispersion control chart

Statistical process control (SPC) methods are useful for improving or maintaining a manufacturing or service process in a stable and satisfactory state. Nowadays, in many industrial applications, it is necessary to simultaneously monitor more than two related quality variables of a process. Multivariate control charts are thus becoming an important research area. Using separate control charts to monitor related quality variables independently is very unreasonable and misleading. The problem of monitoring multivariate statistical process (MSPC) for several related quality variables is of current interest. So far in the literature, a few papers have discussed monitoring process dispersion for cases in which the process has a multivariate normal or non-normal distribution. A new EWMA dispersion control chart is proposed to monitor the process covariance matrix. Moreover, the proposed new EWMA dispersion control chart is independent of the out-of-control process mean vector. It overcomes the problem in many existing covariance matrix control charts of assuming that there are no shifts in the process mean vector which, depending on the existence of shifts in mean, can lead to an increased false alarm rate. The derivation of the new EWMA dispersion control chart is illustrated and its out-of-control detection performance investigated. The proposed new EWMA dispersion control chart performs better than the existing control charts for detecting out-of-control process variances whether the covariances change or not. An example involving semi-conductor data is adopted to demonstrate the application of the proposed new EWMA dispersion control chart.