Monitoring the process coefficient of variation without subgrouping

Abstract

The coefficient of variation (CV), a measure of relative variability, is an important quality characteristic for a manufacturing process especially when the variance becomes a function of the mean. In this paper, for the first time, we develop four memory-type control charts for monitoring the CV of a normal process using individual observations, namely Crosier CUSUM (CC), EWMA, adaptive CC and adaptive EWMA charts. In addition, the sensitivities of these CV charts are also enhanced via an auxiliary information based CV estimator. The run length characteristics of these control charts are computed using Monte Carlo simulations. The proposed CV charts are also applied on a real dataset related to dowel pins.

Keywords:

• Auxiliary information
• average run length
• adaptive and non-adaptive charts
• coefficient of variation
• Monte Carlo simulation
• statistical process control

2010 Mathematics Subject Classification:

• 62N10

Acknowledgments

The authors are thankful to the associate editor and the anonymous reviewers for providing useful comments that led to an improved version of the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).