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DEWMA-MA Control Chart Development within the Range to Improve the Detection of Changes in Process Variation.

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Title: DEWMA-MA Control Chart Development within the Range to Improve the Detection of Changes in Process Variation.
Authors: Phantu, Suganya1 suganya.p@sciee.kmutnb.ac.th, Areepong, Yupaporn2 yupaporn.a@sci.kmutnb.ac.th, Sukparungsee, Saowanit2 saowanit.s@sci.kmutnb.ac.th
Source: IAENG International Journal of Applied Mathematics. Apr2026, Vol. 56 Issue 4, p1316-1324. 9p.
Subjects: Quality control charts, Statistical process control, Simulation methods & models, Process control systems
Abstract: This study examines the performance of four control charts for monitoring process variability: Range (R) chart, Moving Average (MA) chart, Double Exponentially Weighted Moving Average (DEWMA) chart, and hybrid DEWMA-MA chart. The evaluation is conducted through simulation using three performance measures: Average Run Length (ARL), Standard Deviation of Run Length (SDRL), and Median Run Length (MRL). Subgroup sizes of 5, 10, and 15, along with varying moving average widths and smoothing parameters, are considered to assess chart responsiveness under different process conditions. The results confirm that all charts are properly calibrated, with in-control ARL values close to the theoretical standard of 370. However, the DEWMA-MA chart consistently outperforms the others in detecting small-to-moderate shifts, producing shorter ARL, SDRL, and MRL values. For large shifts, performance converges across all methods, though DEWMA-MA exhibits lower detection variability. A real manufacturing dataset further validates the superiority of the DEWMA-MA chart, highlighting its robustness and reliability as a tool for statistical process control. [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: DEWMA-MA Control Chart Development within the Range to Improve the Detection of Changes in Process Variation.
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  Data: <searchLink fieldCode="AR" term="%22Phantu%2C+Suganya%22">Phantu, Suganya</searchLink><relatesTo>1</relatesTo><i> suganya.p@sciee.kmutnb.ac.th</i><br /><searchLink fieldCode="AR" term="%22Areepong%2C+Yupaporn%22">Areepong, Yupaporn</searchLink><relatesTo>2</relatesTo><i> yupaporn.a@sci.kmutnb.ac.th</i><br /><searchLink fieldCode="AR" term="%22Sukparungsee%2C+Saowanit%22">Sukparungsee, Saowanit</searchLink><relatesTo>2</relatesTo><i> saowanit.s@sci.kmutnb.ac.th</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Apr2026, Vol. 56 Issue 4, p1316-1324. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Quality+control+charts%22">Quality control charts</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+process+control%22">Statistical process control</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Process+control+systems%22">Process control systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study examines the performance of four control charts for monitoring process variability: Range (R) chart, Moving Average (MA) chart, Double Exponentially Weighted Moving Average (DEWMA) chart, and hybrid DEWMA-MA chart. The evaluation is conducted through simulation using three performance measures: Average Run Length (ARL), Standard Deviation of Run Length (SDRL), and Median Run Length (MRL). Subgroup sizes of 5, 10, and 15, along with varying moving average widths and smoothing parameters, are considered to assess chart responsiveness under different process conditions. The results confirm that all charts are properly calibrated, with in-control ARL values close to the theoretical standard of 370. However, the DEWMA-MA chart consistently outperforms the others in detecting small-to-moderate shifts, producing shorter ARL, SDRL, and MRL values. For large shifts, performance converges across all methods, though DEWMA-MA exhibits lower detection variability. A real manufacturing dataset further validates the superiority of the DEWMA-MA chart, highlighting its robustness and reliability as a tool for statistical process control. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Apr2026
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              Y: 2026
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