Abstract:
Under the normality assumption, four univariate exponentially moving average single control charts are proposed and they are designed to monitor simultaneously both the process mean and the process variability. The performances of these four charts are evaluated by comparing their average run lengths among themselves as well as to two other competing combination charts. Based on the comparison of the six univariate charts, a multivariate exponentially moving average single control chart is developed as an extension of one of the best univariate charts. This chart performs better than the combination of the two widely used multivariate charts when small changes are of interest. In dealing with positively-skewed distributed data, the direct logarithmic transformation may result in a control chart with inappropriate control parameters in the application of quality control. When a specific interval for the lognormal mean is given, a new method is introduced to set up two control charts and these two charts can monitor a process for which the underlying distribution of the quality characteristic is lognormal.
