APPLICATION OF MULTIVARIATE CONTROL CHARTS FOR MONITORING AN INDUSTRIAL PROCESS

The effective simultaneous monitoring of the many quality characteristics of a production process often depends on statistical tools that are becoming more and more specific. The goal of this paper is to investigate, via an industrial application, if there are significant differences in sensitivity between the use of Multivariate Cumulative Sum (MCUSUM), Multivariate Exponentially Weighted Average (MEWMA) control charts and Hotelling T2 charts in the detection of small changes in the vector of means of the process. In doing this study, we used real data from a machining process. A MCUSUM control chart was applied to monitor the two quality characteristics of this process simultaneously. A MEWMA chart was also applied. The result was compared to the application of the Hotelling T2 chart, which showed that the MCUSUM and MEWMA control charts detected the change sooner. This study was fundamental in defining the best choice between the three charts for the multivariate statistical analysis of this industrial process.The effective simultaneous monitoring of the many quality characteristics of a production process often depends on statistical tools that have become more and more specific. The goal of this paper is to investigate, via an industrial application, whether there are significant differences in sensitivity between the use of Multivariate Cumulative Sum (MCUSUM), Multivariate Exponentially Weighted Average (MEWMA) control charts, and Hotelling T2 charts to detect small changes in the mean vector of a process. Machining process real data were used. A MCUSUM control chart was applied to monitor these two quality characteristics of this process simultaneously. A MEWMA chart was also applied. The result was compared to that of the application of the Hotelling T2 chart, which showed that the MCUSUM and MEWMA control charts detected the change sooner. This study was essential to determine the best option between these three charts for the multivariate statistical analysis of this industrial process