The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. A few papers have proposed control charts that monitor this normalized measure of dispersion. This paper suggests a new method to monitor the CV by means of two one-sided EWMA charts of the coefficient of variation squared γ². Tables are provided for the statistical properties of the EWMA-γ² when the shift size is deterministic or unknown. An example illustrates the use of these charts on real data gathered from a metal sintering process.

Statistical process monitoring (SPM) techniques have been widely used in industry for many decades in order to assess the process stability, as well as the final product quality. Interestingly, SPM techniques have gained popularity in many non-industrial fields providing even more opportunities for research. In this paper, we consider the case of multivariate statistical process monitoring (MSPM) and perform an extensive literature review on MSPM applications in non-industrial fields. More specifically, the aim of this paper is to point out the main non-industrial application fields of MSPM that have appeared in the literature, discuss the main challenges that arise upon applying MSPM outside the industrial environment and provide some thoughts for the future of MSPM outside industry.