Monitoring the Coefficient of Variation Using a Synthetic Exponentially Weighted Moving Average Chart

ABSTRACT Coefficient of variation ( γ ) charts are widely adopted in many industries to monitor processes without a consistent mean ( μ ) and which has a standard deviation ( σ ) that is dependent on μ . The exponentially weighted moving average (EWMA) and synthetic (Syn) γ charts show different strengths, where the EWMA γ chart is sensitive toward small and moderate shifts, while the synthetic γ chart shows better sensitivities toward large shifts. Hence, the Syn‐EWMA γ chart is proposed, where it combines the features of both charts. This paper contributes to the literature by (i) developing the proposed chart's operations; (ii) deriving the formulae of the average run length (ARL), standard deviation of the run length (SDRL), and expected average run length (EARL); and (iii) formulating algorithms that optimize its performance. The proposed chart is shown to outperform the Shewhart, EWMA, and synthetic γ charts across all shifts. Lastly, the implementation on a sintering process is shown.