Design and Application of Mixed Tukey DMA-EWMA Control Charts for Enhanced Process Mean Monitoring.

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Bibliographic Details
Title:	Design and Application of Mixed Tukey DMA-EWMA Control Charts for Enhanced Process Mean Monitoring.
Authors:	Taboran, Rattikarn¹ rattikarntab@mcru.ac.th, Areepong, Yupaporn² yupaporn.a@sci.kmutnb.ac.th, Sukparungsee, Saowanit² saowanit.s@sci.kmutnb.ac.th
Source:	Engineering Letters. May2026, Vol. 34 Issue 5, p1772-1783. 12p.
Subjects:	Statistical process control, Quality control charts, Process control systems, Monte Carlo method
Abstract:	Nonparametric control charts are a type of statistical tool for process monitoring with no need to rely on assumptions about data distributions. Undoubtedly, they are flexible and more suitable in reality. The objectives of this research are to propose a new nonparametric control chart, the Tukey double moving average - exponentially weighted moving average (Tukey DMA-EWMA), and to compare shift detection efficiency and the other control charts, namely Tukey EWMA-DMA, DMA-EWMA, EWMA-DMA, TCC, DMA, and EWMA. This study used Monte Carlo Simulation (MC) to assess efficiency of these control charts under various distributions: normal, Laplace, gamma, and Weibull. Sizes of shifts were set between -3.00 to 3.00. As for the measurement criteria of average run length (ARL) and median run length (MRL). The results revealed as follows: 1) Under normal distribution and Laplace distribution at the shift levels of ±1.00, ±1.50, ±2.00, ±2.50, and ±3.00, Tukey DMA-EWMA had better detection efficiency than the other control charts, 2) Under gamma distribution, Tukey DMA-EWMA had the highest detection efficiency on the shifted averages of the process at all shift levels and 3) Applying the subject control chart to real industrial data conformed to the results of the simulations. This indicated the suitability of the proposed control chart. This implied that Tukey DMA-EWMA is an efficient option for process monitoring when data is not in accordance with normal distribution or when flexibility is required for real use. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

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Abstract:	Nonparametric control charts are a type of statistical tool for process monitoring with no need to rely on assumptions about data distributions. Undoubtedly, they are flexible and more suitable in reality. The objectives of this research are to propose a new nonparametric control chart, the Tukey double moving average - exponentially weighted moving average (Tukey DMA-EWMA), and to compare shift detection efficiency and the other control charts, namely Tukey EWMA-DMA, DMA-EWMA, EWMA-DMA, TCC, DMA, and EWMA. This study used Monte Carlo Simulation (MC) to assess efficiency of these control charts under various distributions: normal, Laplace, gamma, and Weibull. Sizes of shifts were set between -3.00 to 3.00. As for the measurement criteria of average run length (ARL) and median run length (MRL). The results revealed as follows: 1) Under normal distribution and Laplace distribution at the shift levels of ±1.00, ±1.50, ±2.00, ±2.50, and ±3.00, Tukey DMA-EWMA had better detection efficiency than the other control charts, 2) Under gamma distribution, Tukey DMA-EWMA had the highest detection efficiency on the shifted averages of the process at all shift levels and 3) Applying the subject control chart to real industrial data conformed to the results of the simulations. This indicated the suitability of the proposed control chart. This implied that Tukey DMA-EWMA is an efficient option for process monitoring when data is not in accordance with normal distribution or when flexibility is required for real use. [ABSTRACT FROM AUTHOR]
ISSN:	1816093X