Design and Implementation of an EWMA Control Chart for Zero-Inflated Count Data in High-Quality Processes
Raza M.A. Sattar A. Nawaz T. Tahir M.A. Farooq M. Bhatti S.H.
July 2025John Wiley and Sons Ltd
Quality and Reliability Engineering International
2025#41Issue 52164 - 2181 pp.
The Poisson distribution is often employed to model count data, but it may not accurately represent a dataset with frequent occurrences of zero counts. This limitation often arises in high-quality processes where the production of nonconforming items is minimal. To address this issue, modified forms of existing distributions such as the zero-inflated geometric (ZIG) distributions, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) have been developed to more accurately capture the zero-inflated (ZI) count data. Control charts under ZIP distribution are effective for monitoring processes with zero defects. However, determining whether the data exhibit over-dispersion or under-dispersion is often challenging. To address this challenge and accommodate various dispersion patterns in zero-defect datasets, a flexible distribution called the ZI Conway–Maxwell–Poisson (ZICOMP) distribution is developed in the literature. This distribution is capable of modeling datasets that are over-dispersed, under-dispersed, or equi-dispersed. In this study, the ZICOMP distribution is integrated with the exponentially weighted moving average (EWMA) control charting structure to efficiently monitor the processes involving ZI count data, regardless of the dispersion level. Extensive Monte Carlo simulations are performed to evaluate the performance of the proposed chart under different parameter settings. Additionally, two real-life applications are provided to demonstrate the practical implementation and effectiveness of the proposed chart for both over-dispersion and under-dispersion scenarios.
Conway–Maxwell–Poisson distribution , count data , exponentially weighted moving average control chart , Monte Carlo simulation , zero-inflation
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Department of Statistics, Government College University Faisalabad, Faisalabad, Pakistan
School of Digital Technologies, Narxoz University, Almaty, Kazakhstan
Department of Statistics, College of Science, Sultan Qaboos University, Muscat, Oman
Department of Statistics, Government College University Lahore, Lahore, Pakistan
College of Statistical Sciences, University of the Punjab, Lahore, Pakistan
Department of Statistics
School of Digital Technologies
Department of Statistics
Department of Statistics
College of Statistical Sciences
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