Quality control is commonly divided into off-line activities, synonymous
with robust design (RD), and on-line procedures, also known as statistical
process control (SPC). Most research in both areas of quality control has
dealt with single variables. Since most complex systems are multivariate
in nature, there is an increasing need for user friendly multivariate
techniques.

The multivariate quadratic loss function (MQL) is a popular multivariate
technique in static RD and has occasionally been applied to multivariate
SPC. In both areas we integrate the contents of the MQL into specially
constructed principal components called loss-scaled principal components
(LSPC). We examine how well a subset of these LSPC approximate the
expected value of MQL and apply them to a RD problem featuring six
responses and eight predictor variables. We also show when
LSPCs can quickly detect and accurately diagnose shifts in location and
dispersion in multivariate SPC.