A new exponentially weighted moving average control chart to monitor count data with applications in healthcare and manufacturing

Abstract

Attribute control charts are widely used to monitor count data. Many distributions are proposed to model and monitor count data. This article has developed an exponentially weighted moving average (EWMA) control chart under Generalized Conway–Maxwell–Poisson (GCOMP) distribution and named as the GCOMP-EWMA chart. The GCOMP distribution is an extension of the Conway–Maxwell–Poisson (COMP) distribution and is a longer-tailed model than the COMP distribution. The GCOMP distribution can also model the shorter tail behaviour in count data. Considering the tail behaviour, the in-control and out-of-control performance of the GCOMP-EWMA chart have been evaluated for over- and under-dispersed count data. Without using the zero-inflation property, the GCOMP-EWMA chart performs efficiently in monitoring zero-inflated count data compared to existing zero-inflated models-based control charts. Finally, illustrative examples demonstrate the practical applications of the GCOMP-EWMA charts in different fields.

KEYWORDS:

• Count data
• control charts
• longer tail
• over-dispersion
• under-dispersion
• zero-inflation

Disclosure statement

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