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RESEARCH ARTICLE

An efficient adaptive exponentially weighted moving average chart for effective fault detection in the process mean

Tahir Abbas1* ,  Hafiz Zafar Nazir2 ,  Muhammad Abid3 ,  Muhammad Riaz4
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1 Department of Mathematics, College of Sciences, University of Sharjah, Sharjah , United Arab Emirates
2 Department of Statistics, Faculty of Sciences, University of Sargodha, Sargodha, Punjab , Pakistan
3 Department of Statistics, Faculty of Physical Science, Government College University Faisalabad, Faisala-bad, Punjab , Pakistan
4 Department of Mathematics, College of Computing and Mathematics, King Fahd University of Petroleum and Minerals, Dhahran , Saudi Arabia
IJOCTA, 026160063 https://doi.org/10.36922/IJOCTA026160063
Received: 16 April 2026 | Revised: 19 June 2026 | Accepted: 23 June 2026 | Published online: 13 November 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

The exponentially weighted moving average (EWMA) control charts with adaptive charting structures have attracted attention from the research community for their dynamically adjusted smoothing parameters, which improve sensitivity to both small and large process shifts. Unlike a traditional EWMA chart, it can maintain stability in normal conditions and detect deviations quickly, thereby improving monitoring efficiency. In this study, we propose a memory-based control chart for the monitoring of the process mean of a normally distributed process. It estimates the mean shifts with an EWMA-based estimator and then finds the smoothing constant for plotting the EWMA statistics using a proposed continuous function. Monte Carlo simulations are conducted to evaluate the performance of the proposed adaptive EWMA (AEWMA) chart under different shifts in the process mean. The run-length profiles metric, along with overall performance measures, was used as a performance evaluation tool, and the proposed AEWMA chart outperformed the competing methods. A real data set from a semiconductor manufacturing process, where the quality characteristic of interest is the wafer flow width, illustrates the applicability in practice.

Graphical abstract
Keywords
Adaptive chart
Process monitoring
Monte Carlo simulations
Run length matrix
Statistical process control
Funding
This work was supported by the Office of the Vice Chancellor for Research and Graduate Studies (VCRG) at the University of Sharjah, United Arab Emirates, under Project 24021440167.
Conflict of interest
The author declares no conflicts of interest that could have influenced the work reported in this paper.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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