On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

Control chart (CC) is used to monitor the special causes that arise during the process monitoring.These special causes produce continual shifts in the process parameters that last until it is identified and removed. There is a need for such techniques, which present the true representation of the entire process. Rational subgrouping is an essential concept in Statistical Process Control (SPC) which is seldom overlooked by the practitioner. Hence, most of the manufacturing, engineering, and production processes give output products in the form of batches over smaller intervals of time. The aim of this study is to provide a median based design for Tukey and Tukey-EWMA control charts under subgrouping. It will use the idea of boxplot to monitor the process behavior. This study also provides a brief discussion regarding selecting and forming subgroups from the process data. The performance of the median based Tukey and Tukey-EWMA charts are judged using Average, Median and Standard-Deviation run-length as performance measures. We have considered subgroup sizes of m=1,5 &10 at pre-specified ARL0 equal to 370. To real-life applications of the median based tukey designs are also presented to show their implementation in food manufacturing and hard-bake processes.2

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“…Abbasi at el. [26] presented some modification to classical EWMA design to improve the sensitivity of memory chart. Mahmood at el.…”

Section: Significance Of Subgrouping In Control Chartsmentioning

confidence: 99%

On the Performance of Median Based Tukey and Tukey-EWMA Charts Under Rational Subgrouping

et al. 2019

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“…These control chart had a good performance to detect a small shift in the process mean. For more recent research on the improved control chart, we referred to [5] and [6].…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Information Based Generally Weighted Moving Average for Process Mean

Aziza¹,

Kusuma

Soraya

2023

Jurnal Statistika Universitas Muhammadiyah Semarang

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The univariate mean process monitoring is only used the information from the study variable. One of the univariate control chart that used to monitor the mean process is GWMA control chart. But, in this research, we need to monitor process mean using the information from the study variable and information on the adding or auxiliary variable. The enhanced control chart in this research named AIB-GWMA control chart. In this research, we also made a comparison between the GWMA and AIB-GWMA to know the sensitivity and effectiveness of these control chart. The comparison is used to know the effect of the auxiliary variable in process monitoring. The performance of these control chart is evaluated using Average Run Length with help of Monte Carlo simulation. The result of this study is AIB-GWMA has a smaller ARL than the GWMA control chart. It showed that AIB-GWMA is faster to detect a shift in mean process. In further study, we recommended to enhance the performance of the AIB-GWMA by extending the current work to the AIB-MaxGWMA, so it is possible to monitor process mean and variance simultaneously.

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“…Riaz (2015) developed a new nonparametric EWMA chart based on sign statistic. Abbasi et al (2017) enhanced the performance of the NPEWMA sign chart by applying fast initial response feature. Abid et al (2017b, 2018) improved the efficiency of the NPEWMA-SR and NPCUSUM-SR charts under ranked set sampling, respectively.…”

Section: Introductionmentioning

confidence: 99%

Enhanced nonparametric control charts under simple and ranked set sampling schemes

Abbas¹,

Nazir

Abid

et al. 2020

Transactions of the Institute of Measurement and Control

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Investigation and removal of unnatural variation in the processes of manufacturing, production and services require application of statistical process control. Control charts are the most famous and commonly used statistical process control tools to trace changes in the manufacturing and nonmanufacturing processes parameter(s). The nonparametric control charts become necessary when the distribution of underlying process is unknown or questionable. The nonparametric charts are robust alternative along with holding property of quick shift detection ability in process parameter(s). In this article, we have proposed nonparametric double exponentially weighted moving average chart based on Wilcoxon signed rank test under simple and ranked set sampling schemes for efficient monitoring of the process location. The proposed control charts are compared with classical exponentially weighted moving average, double exponentially weighted moving average, nonparametric exponentially weighted moving average sign, nonparametric exponentially weighted moving average signed rank, nonparametric cumulative sum signed rank charts using average run length and some other characteristics of run length distribution as performance measures. Comparison reveals that the proposed control charts performs better to detect all kinds of shifts in the process location than existing counterparts. A real-life application related to manufacturing process (the variable of interest is the diameter of piston ring) is also provided for the practical implementation of the proposed chart.

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On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

Cited by 9 publications

References 41 publications

On the Performance of Median Based Tukey and Tukey-EWMA Charts Under Rational Subgrouping

On the Performance of Median Based Tukey and Tukey-EWMA Charts Under Rational Subgrouping

Auxiliary Information Based Generally Weighted Moving Average for Process Mean

Enhanced nonparametric control charts under simple and ranked set sampling schemes

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