We propose a new multivariate CUSUM control chart, which is based on selfadaption of its reference value according to the information of current process readings, to quickly detect the multivariate process mean shifts. By specifying the minimum magnitude of the process mean shift in terms of its non-centrality parameter, our proposed control chart can achieve an overall performance for detectin...

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 ...

Multivariate control charts are used for monitoring multiple series simultaneously, for the purpose of detecting shifts in the mean vector in any direction. In the context of disease outbreak detection, interest is in detecting only an increase in the process means. Two practical approaches for deriving directional Hotelling charts are Follmann’s correction and Testik and Runger’s quadratic pro...

Abstract: In this paper, the economic design of MSE control chart is extended to the multivariate case. The important feature of this control chart is that it uses the target value instead of the process mean. According to Taguchi's viewpoint, any deviation from the target value represents a kind of loss. Therefore, we construct the model of economic design by considering not only the control c...

We developed a multivariate moving average control chart to monitor the mean vector of a multivariate process in this study. The proposed chart is based on Hotelling’s 2 T statistic. A simulation study is conducted to evaluate the average run length performance of the proposed chart. The proposed chart performs better than Hotelling’s 2 T chart, 2 T chart with runs rules and the synthetic 2 T c...