A double generally weighted moving average control chart for monitoring the process variability

Alevizakos, Vasileios; Chatterjee, Kanu; Koukouvinos, Christos; Lappa, Angeliki

doi:10.1080/02664763.2022.2064977

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…Anwar et al [23] and Anwar et al [24] proposed mixed memory-type control charts for the simultaneous monitoring of more than one parameters using auxiliary information. A double generally weighted moving average (GWMA) control chart for monitoring dispersion parameters was designed by Alevizakos et al [25]. Akhtar et al [26] evaluated the EWMA control chart by using log-normal distributions with estimated parameters to monitor process variability.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Dispersion Monitoring Structures Based on Modified Successive Sampling: Application to Fertilizer Production Process

et al. 2023

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In this era of Industry 4.0, efficient and affordable monitoring solutions are needed for the surveillance of manufacturing/service operations. In general, memory-type control charts outperform memoryless control charts when it comes to determining the changes in location and dispersion parameters of symmetrically distributed processes. Before monitoring the process location, it is essential to monitor the process dispersion, since the latter presumes that the process variance remains stable. In practice, the modified successive sampling (MSS) mechanism is preferred over simple random sampling for its cost-effectiveness and efficiency. This study was designed in order to propose moving average and double moving average control charts based on the MSS mechanism for monitoring the dispersion parameter. The performance of the proposed charts is evaluated using run-length measures, and a comparison is made with an existing control chart based on MSS and repetitive sampling. Furthermore, the application of the designed moving and double moving average charts is demonstrated using a case study related to fertilizer production. It is observed that the proposed double moving average control chart performs better than the other control charts designed under the MSS and repetitive sampling schemes.

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Section: Introductionmentioning

confidence: 99%

Enhanced Dispersion Monitoring Structures Based on Modified Successive Sampling: Application to Fertilizer Production Process

et al. 2023

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“…Several authors have also utilized this logarithmic transformation to propose control charts for monitoring the process variability as well. Abbas, Riaz and Does [13] recommended the CS-EWMA chart, Tariq et al [14] presented the S 2 -HEWMA chart, Chatterjee, Koukouvinos and Lappa [15] introduced the S 2 -TEWMA chart, while Alevizakos et al [16,17] developed the S 2 -GWMA and S 2 -Double GWMA (S 2 -DGWMA) charts for monitoring the process variability. Additionally, a multitude of dispersion control charts have been introduced in the literature.…”

Section: Introductionmentioning

confidence: 99%

Simulation modeling with memory-type control charts for monitoring the process variability

Chatterjee,

Hached,

Koukouvinos

et al. 2023

J. Appl. Artif. Intell.

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Memory-type control charts, renowned for their effectiveness in identifying small deviations in the process variance, are commonly used to monitor the process variability. In this article, we introduce a new tool, the Quadruple Exponentially Weighted Moving Average (QEWMA) chart, which is designed for the specific purpose of monitoring changes in the process variability. We refer to this chart as the -QEWMA chart. The performance of the -QEWMA chart is assessed through an extensive series of Monte-Carlo simulations, carefully considering the run-length distribution. Comparing it with other well-known memory-type charts, it becomes evident that the -QEWMA chart excels in its ability to effectively detect small shifts in the process dispersion. To illustrate the practical application of this chart, we provide an example.

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The generally weighted moving average control chart for monitoring the process mean of autocorrelated observations

Sheu

Hsu

2023

Ann Oper Res

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A double generally weighted moving average control chart for monitoring the process variability

Abstract: This section contains additional results for the S 2 -DGWMA, S 2 -GWMA, CS-EWMA, and S 2 -TEWMA control charts.

Cited by 4 publications

References 46 publications

Enhanced Dispersion Monitoring Structures Based on Modified Successive Sampling: Application to Fertilizer Production Process

Enhanced Dispersion Monitoring Structures Based on Modified Successive Sampling: Application to Fertilizer Production Process

Simulation modeling with memory-type control charts for monitoring the process variability

The generally weighted moving average control chart for monitoring the process mean of autocorrelated observations

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