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Abstract <p>In many actual situations, the underlying distribution of a quality parameter deviates from normalcy or is unknown. Nonparametric control charts are commonly used in such instances by practitioners. To solve problems and develop tools to support various situations, we develop nonparametric control charts that combine MEM and MME charts using the sign statistic (MEM-SN and MME-SN) to track changes in process location. Monte Carlo simulations evaluate the chart’s performance based on the average run length (ARL). The MEM-SN and MME-SN charts outperformed the other control charts in identifying shifts in the process mean under different symmetrical and asymmetrical distributions. Three real datasets are provided to illustrate the mechanism of the chart under different distributions, including normal, exponential, and gamma distributions.</p>

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Nonparametric Mixed Moving Average–Exponentially Weighted Moving Average Control Chart with Applications

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Weerawat Sudsutad,
  • Yupaporn Areepong,
  • Saowanit Suparungsee

摘要

Abstract

In many actual situations, the underlying distribution of a quality parameter deviates from normalcy or is unknown. Nonparametric control charts are commonly used in such instances by practitioners. To solve problems and develop tools to support various situations, we develop nonparametric control charts that combine MEM and MME charts using the sign statistic (MEM-SN and MME-SN) to track changes in process location. Monte Carlo simulations evaluate the chart’s performance based on the average run length (ARL). The MEM-SN and MME-SN charts outperformed the other control charts in identifying shifts in the process mean under different symmetrical and asymmetrical distributions. Three real datasets are provided to illustrate the mechanism of the chart under different distributions, including normal, exponential, and gamma distributions.