Development of a VSS-EWMA chart for coefficient of variation with application to production process

This study introduces a novel Variable Sample Size Exponentially Weighted Moving Average (VSS-EWMA) control chart for monitoring the coefficient of variation, termed as Dynamic Adaptive CV (DACV) chart. Tailored for dynamic production settings where both the process mean and variability are subject to change, the proposed chart integrates an adaptive sampling strategy within the EWMA framework, allowing real-time adjustment of sample size in response to process conditions. Comparative analysis with the conventional Fixed Sample Size EWMA (FEWMA) chart reveals that DACV chart exhibits enhanced sensitivity in detecting small to moderate shifts in variability. Its performance is rigorously evaluated using Average Run Length (ARL), Standard Deviation of Run Length (SDRL), and run-length percentiles. Visualizations through heat maps further affirm its robustness across a wide range of shift magnitudes and smoothing parameters. A real-world application using semiconductor manufacturing data demonstrates the practical utility of DACV chart, underscoring its potential in contemporary quality monitoring systems.

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Publication Details

Journal
Scientific Reports
Published
2025-11-03
DOI
https://doi.org/10.1038/s41598-025-23704-6
Primary Topic
Advanced Statistical Process Monitoring
Type
article
Field-Weighted Citation Impact
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article

Development of a VSS-EWMA chart for coefficient of variation with application to production process

Amna Safeer, Muhammad Nabi, Maysaa Elmahi Abd Elwahab
Scientific Reports
Advanced Statistical Process Monitoring
article

Development of a VSS-EWMA chart for coefficient of variation with application to production process

Amna Safeer, Muhammad Nabi, Maysaa Elmahi Abd Elwahab
article en

Abstract

This study introduces a novel Variable Sample Size Exponentially Weighted Moving Average (VSS-EWMA) control chart for monitoring the coefficient of variation, termed as Dynamic Adaptive CV (DACV) chart. Tailored for dynamic production settings where both the process mean and variability are subject to change, the proposed chart integrates an adaptive sampling strategy within the EWMA framework, allowing real-time adjustment of sample size in response to process conditions. Comparative analysis with the conventional Fixed Sample Size EWMA (FEWMA) chart reveals that DACV chart exhibits enhanced sensitivity in detecting small to moderate shifts in variability. Its performance is rigorously evaluated using Average Run Length (ARL), Standard Deviation of Run Length (SDRL), and run-length percentiles. Visualizations through heat maps further affirm its robustness across a wide range of shift magnitudes and smoothing parameters. A real-world application using semiconductor manufacturing data demonstrates the practical utility of DACV chart, underscoring its potential in contemporary quality monitoring systems.

Scientific ReportsVol. 15(1)
Princess Nourah bint Abdulrahman University (SA), Brock University (CA), Khost University (AF)
Openalex Percentile: Top 68%
Advanced Statistical Process Monitoring
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