On the location‐based memory type control charts under modified successive sampling scheme

Hyder, Mehvish; Mahmood, Tahir; Butt, Muhammad Moeen; Raza, Syed Muhammad Muslim; Abbas, Nasir

doi:10.1002/qre.3049

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…This sampling procedure will be carried out until desired sample size is achieved. Patterson [36], Rao and Graham [37], Choudhary et al [38], Yaqub et al [32], Abbas et al [33] and Hyder et al [34] described some modifications of successive sampling.…”

Section: Modified Successive Samplingmentioning

confidence: 99%

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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: Modified Successive Samplingmentioning

confidence: 99%

“…The modified form of the successive sampling scheme is more effective than other existing sampling techniques and is specially designed to monitor process parameters (location or dispersion). For the real-time surveillance of the location parameter, memory-type control charts based on MSS were designed by Hyder et al [34].…”

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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“…Riaz et al [31] showed that the linear profile monitoring methods under the MSS scheme outperformed the SRS schemes linear profile monitoring methods. Recently, Hyder et al [17] introduced new moving average control charts based on the MSS scheme for monitoring the processs dispersion parameter.…”

Section: Introductionmentioning

confidence: 99%

On moving average based location charts under modified successive sampling

Mahmood,

Hyder,

Raza

et al. 2024

Hacettepe Journal of Mathematics and Statistics

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Ceramics are made up of water, clay, and powders. These are categorized as non-metallic and inorganic materials. It is revealed in the literature that Longquan celadon glaze had irregular cracks in glaze layers due to the relatively high content of N a2O. Therefore, it is necessary to monitor the influence of N a2O in the ceramic process. Control charts are a possible tool to monitor the changes in the ceramic process. For single event issues, simple random sampling strategy is utilized; however, modified successive sampling is preferred as the favored sampling strategy at regular intervals of time when the quality of any product is evaluated. Hence, this paper is designed to propose moving average ({MA}_{MSS(S)}) and double moving average ({DMA}_{MSS(S)}) based control charts to detect small to moderate location shifts using the modified successive sampling technique. We have highlighted the performance evaluations of designed control charts with respect to run-length metrics, and their comparison has been made with the existing ShewhartMSS(S) control chart. The results revealed that the ({DMA}_{MSS(S)}) performs more efficiently as compared to the ShewhartMSS(S) and ({MA}_{MSS(S)}) control charts. Further, to demonstrate the application of the designed charts, a dataset of the chemical composition of the ceramic is also utilized.

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“…Moreover, the generally weighted moving average (GWMA) and its extension as a double GWMA chart based on exceedance statistics are proposed by Chakraborty et al 17 and Masoumi Karakani et al 18 Researchers highly prefer CUSUM charts as they contain information about historical observations like EWMA charts. 19 Generally, CUSUM charts outperform Shewhart charts in detecting small shifts; see Hyder et al 20 On the other hand, the in-control (IC) robustness of parametric CUSUM charts is insufficient. 21 Therefore, there have been many studies related to univariate nonparametric CUSUM charts.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers highly prefer CUSUM charts as they contain information about historical observations like EWMA charts 19 . Generally, CUSUM charts outperform Shewhart charts in detecting small shifts; see Hyder et al 20 . On the other hand, the in‐control (IC) robustness of parametric CUSUM charts is insufficient 21 .…”

Section: Introductionmentioning

confidence: 99%

A bivariate CUSUM control chart based on exceedance statistics

Erem

Mahmood

2023

Quality & Reliability Eng

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In manufacturing systems, statistical process control (SPC) is used to detect unusual observations in the process. Most control charts are composed based on the normality assumptions of the observations. However, checking the normality assumption in multivariate control systems cannot be practical in real‐life applications. As a result, we propose a nonparametric bivariate cumulative sum control chart (CUSUM) based on exceedance statistics for detecting the shifts in location parameters. Since the proposed chart (BCUSUM‐EX) relied on exceedance statistics and is nonparametric, the use of the chart is straightforward and practical for users. Moreover, due to the nature of CUSUM charts, the proposed chart contains information about historical and current observations. The performance of the BCUSUM‐EX chart is compared with the spatial sign CUSUM (SS‐CUSUM) control chart under some well‐known bivariate distributions such as bivariate normal, t, and gamma. The BCUSUM‐EX chart's performance is better than the SS‐CUSUM control chart regarding run length characteristics. Lastly, to show the importance of the proposed control chart, the BCUSUM‐EX chart has been applied to the aluminium electrolytic capacitor manufacturing process.

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On the location‐based memory type control charts under modified successive sampling scheme

Cited by 9 publications

References 32 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

On moving average based location charts under modified successive sampling

A bivariate CUSUM control chart based on exceedance statistics

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