Monitoring Schemes for Multivariate Process Management: An Applied Review

When there is a serious cause manifested in a process and it makes the process departs to an out-of-control condition, an effective root cause analysis could lead the process management to identify and eliminate the serious cause. When a change takes place in a multivariate process, while several correlated variables exist, the root-cause analysis of the process relatively is more challenging compared to the case of a univariate process. Considering an out-of-control multivariate process, one can experience an effective root-cause analysis if only a comprehensive scheme allows detecting the out-of-control condition, identifying the change point, diagnosing the variable(s) contributing to the unnatural condition and distinguishing the shift direction all simultaneously. Although statistical approach has provided effective solution for the univariate process, the approach has not provided a comprehensive solution in which a p-variate process is considered. The multivariate literature indicates that the solution based on the soft computing is illustrated more effectively in comparison with the statistical approach. This research approached practically shows only one of the scheme among the several schemes proposed in the multivariate literature is able to trigger simultaneously all the required signals leading to an effective root-cause analysis. The extensive literature review on the multivariate environment led the authors to represent the comprehensive scheme. The out-of-control ARL criterion is used to evaluate the performance of the scheme compared to the performance of a traditional scheme when the monitoring of the correlated quality specifications in a real car body manufacturing process has been investigated.