Overview: MVPMONITOR Procedure

The MVPMONITOR procedure is used in conjunction with the MVPMODEL procedure to monitor multivariate process variation over time in order to determine whether the process is stable or to detect and diagnose changes in a stable process.

The MVPMONITOR procedure produces control charts for multivariate process data. It reads output data sets that contain statistics and principal components model information and that were created by the MVPMODEL procedure, which is described in Chapter 10, The MVPMODEL Procedure. The MVPMONITOR procedure creates two multivariate control charts: charts and SPE (squared prediction error) charts. It can also create contribution plots, in addition to score plots in some cases.

Multivariate control charts detect unusual variation that would not be uncovered by individually monitoring the variables with univariate control charts, such as Shewhart charts. A major impetus in the development of multivariate control charts is the inadequacy of individual univariate control charts when working with correlated measurement variables. A multivariate control chart can detect changes in the linear relationships of the variables in addition to their marginal means and variances.

The multivariate control charts produced by the MVPMONITOR procedure are based on principal components models which reduce the dimensionality of the data by projecting the process measurements to a low-dimensional subspace that is defined by a small number of principal components. This subspace is also known as the model hyperplane.

The principal components approach offers several advantages over the construction of the classical chart:

• It avoids computational issues that arise when the process variables are collinear and their covariance matrix is nearly singular.

• This approach offers diagnostic tools for interpreting unusual values of .

• By projecting the data to a low-dimensional subspace, a principal components model more adequately describes the variation in a multivariate process, which is often driven by a small number of underlying factors which are not directly observable.