multivariate process capability indices (mpci) show how well a manufacturing process can meet specifica-tion limits when quality characteristics enclose a relative correlation. process capability is an important and commonly used metric for assessing and improving the quality of a production process. when quality charac-teristics of a product are correlated then an attractive comes close to mpc...

Two independent methods for improving quality are engineering process control (EPC) and statistical process control (SPC). The first method tries to minimize variability by handling process variables so as to keep the outputs of the process on target. While the latter method, SPC does the same basic task of minimizing variability by supervising and eradicating the assignable causes of variation...

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the...

This paper develops a new multivariate statistical process control (SPC) methodology based on adapting the LASSO variable selection method to the SPC problem. The LASSO method has the sparsity property that it can select exactly the set of nonzero regression coefficients in multivariate regression modeling, which is especially useful in cases when the number of nonzero coefficients is small. In...

When performing process monitoring, the classical approach of multivariate statistical process control (MSPC) explicitly assumes the normal operating conditions (NOC) to be distributed normally. If this assumption is not met, usually severe out-of-control situations are missed or incontrol situations can falsely be seen as out-of-control. Combining mixture modelling with MSPC (MM-MSPC) leads to...