A control chart for bivariate discrete data monitoring.

Abstract

Control charts are sophisticated graphical tools used to detect and control aberrant variations. Different control schemes are designed to continuously monitor and improve the process stability and performance. This study proposes a bivariate exponentially weighted moving average chart for joint monitoring of the mean vector of Gumbel's bivariate geometric (GBG) data. The performance of the proposed chart is compared with Hotelling's $T^2$ chart. The results of the study indicated that the proposed control chart performs uniformly and substantially better than Hotelling's $T^2$ chart. In addition to two real-life examples, an example based on simulated data is also considered and compared to existing charts to verify the superiority of the proposed chart. Based on the comparisons, it turns out that the MEWMA (GBG) chart outperforms Hotelling's $T^2$ chart and individual EWMA control chart.