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<p>Effectively monitoring process dispersion is critical in ensuring high-quality production in various industrial sectors. This study presents an innovative approach by developing a Dynamic Exponentially Weighted Moving Average (EWMA) control chart with a Variable Sample Size (VSS) to monitor process dispersion. The proposed method enhances the detection capability of small shifts in the coefficient of variation (CV) by adjusting the sample size based on the observed process behavior. Extensive Monte Carlo simulations demonstrate the superior performance of the VSS-EWMA chart over traditional Fixed Sample Size (FSS) EWMA charts, highlighting its robustness in identifying process variability. The practical application of the proposed control chart is validated through empirical data, showing significant improvements in process stability and product quality. This research advances statistical process control by providing a more responsive and efficient real-time quality monitoring and improvement tool.</p>

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Adaptive VSS-EWMA control chart for monitoring the process dispersion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Abdullah Ali H. Ahmadini,
  • Imad Khan,
  • Abdulrahman Obaid Alshammari,
  • Hadeel AlQadi,
  • Wojciech Sumelka

摘要

Effectively monitoring process dispersion is critical in ensuring high-quality production in various industrial sectors. This study presents an innovative approach by developing a Dynamic Exponentially Weighted Moving Average (EWMA) control chart with a Variable Sample Size (VSS) to monitor process dispersion. The proposed method enhances the detection capability of small shifts in the coefficient of variation (CV) by adjusting the sample size based on the observed process behavior. Extensive Monte Carlo simulations demonstrate the superior performance of the VSS-EWMA chart over traditional Fixed Sample Size (FSS) EWMA charts, highlighting its robustness in identifying process variability. The practical application of the proposed control chart is validated through empirical data, showing significant improvements in process stability and product quality. This research advances statistical process control by providing a more responsive and efficient real-time quality monitoring and improvement tool.