  In practice, there are processes where the in-control mean and standard
deviation of a quality characteristic is not stable. In such cases, the
coefficient of variation (CV) is a more appropriate measure for assessing
process stability. In this paper, we consider the statistical design of Run
Rules based control charts for monitoring the CV of multivariate data. A Markov
chain approach is used to evaluate the statistical performance of the proposed
charts. The computational results show that the Run Rules based charts
outperform significantly the standard Shewhart control chart. Moreover, by
choosing an appropriate scheme, the Run Rules based charts perform better than
the Rum Sum control chart for monitoring the multivariate CV. An example in a
spring manufacturing process is given to illustrate the implementation of the
proposed charts.

Applications;Computation