On Enhanced Interquartile Range Charting for Process Dispersion

Riaz, Muhammad

doi:10.1002/qre.1598

Cited by 19 publications

(10 citation statements)

References 40 publications

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“…Recently, Riaz has proposed improved Shewhart‐type charts using AIB location estimators. For more interesting works on the AIB charts, we refer to Haq and Khoo, Abbasi and Riaz, Arshad et al ., Riaz et al ., Riaz, and Riaz et al ., to name a few.…”

Section: Introductionmentioning

confidence: 99%

A New Maximum EWMA Control Chart for Simultaneously Monitoring Process Mean and Dispersion Using Auxiliary Information

Haq

2017

Quality & Reliability Eng

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The maximum exponentially weighted moving average (MaxEWMA) control charts have gained considerable attention for simultaneously detecting both increases and decreases in the mean and/or dispersion of a process. In this paper, we propose a new auxiliary information-based (AIB) MaxEWMA control chart, called the AIB-MaxEWMA chart. The AIB-MaxEWMA chart encompasses the existing MaxEWMA chart. Extensive Monte Carlo simulations are performed to evaluate the average run length, standard deviation of the run length, and diagnostic abilities of the AIB-MaxEWMA chart. An extensive comparison reveals that the AIB-MaxEWMA chart performs uniformly better than the MaxEWMA chart. An example is also used to explain the implementation and working of the AIB-MaxEWMA chart.A. HAQ research papers on the AIB control charts now exist in the literature. Riaz 10 constructed a new AIB Shewhart chart using the regression estimator for improved process mean monitoring, and it has been shown that the AIB Shewhart chart surpasses the classical Shewhart chart. On similar lines, Riaz and Does 11 suggested a Shewhart-type dispersion chart using a ratio-type variance estimator for phase-I quality control. It turned out that the AIB dispersion chart is more powerful than the existing Shewhart unbiased sample variance chart. Ahmad et al. 12 compared the performances of several AIB Shewhart-type dispersion charts-based on the ratio-type variance estimators-with that of the existing process variance control charts, and have suggested using AIB dispersion charts for precisely monitoring the process dispersion. The AIB EWMA chart for monitoring the process mean was suggested by Abbas et al. 13 -the AIB EWMA chart is at least as sensitive as the classical EWMA chart. For efficiently monitoring the process median, Ahmad et al. 14 have suggested several improved AIB Shewhart charts using different ratio-type estimators. Recently, Riaz 15 has proposed improved Shewhart-type charts using AIB location estimators. For more interesting works on the AIB charts, we refer to Haq and Khoo, Abbasi and Riaz, Arshad et al., Riaz et al., Riaz, and Riaz et al.,[16][17][18][19][20][21] to name a few.In this paper, we propose a new MaxEWMA chart for simultaneously monitoring the process mean and dispersion of a normally distributed process using the auxiliary information on a single correlated auxiliary variable, named the AIB-MaxEWMA chart. Monte Carlo simulations are used to compute the run length profiles, including the average run length (ARL) and standard deviation of the run length (SDRL) of the proposed control chart. We show that the AIB-MaxEWMA chart encompasses the existing MaxEWMA chart. In addition, the AIB-MaxEWMA chart turns out to be as sensitive as the MaxEWMA chart.The rest of the paper is organized as follows: the MaxEWMA chart is reviewed in Section 2. Section 3 suggests an AIB-MaxEWMA chart. The performance and comparative studies are conducted in Section 4. An illustrative example is given in Section 5 to explain the working and implementation of ...

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Section: Introductionmentioning

confidence: 99%

A New Maximum EWMA Control Chart for Simultaneously Monitoring Process Mean and Dispersion Using Auxiliary Information

Haq

2017

Quality & Reliability Eng

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“…They have concluded that the performance of R and S charts is highly affected by the non‐normal cases, while IQR chart performs better than R and S charts. The dispersion parameter monitoring by using IQR has been discussed by various authors in literature, for example 19,31–34 . For the efficient monitoring of the dispersion parameter, Riaz 33 proposed the Shewhart charting structure through the use of a set of additional information IQR control charts under normal, t , and gamma‐distributed processes.…”

Section: Introductionmentioning

confidence: 99%

“…Hussain et al 19 continued Riaz's 33 work and proposed EWMA charting structure by using auxiliary information‐based IQR charts and compared the efficiency of the proposed charts with usual R and S charts under both contaminated and uncontaminated scenarios. Consequently, as per our best knowledge, there has been no research in introducing the concept of auxiliary information‐based IQR control charts into the CUSUM charting structure for dispersion monitoring.…”

Section: Introductionmentioning

confidence: 99%

IQR CUSUM charts: An efficient approach for monitoring variations in aquatic toxicity

Hussain

Sun

Mahmood

et al. 2021

Journal of Chemometrics

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Toxicant testing is a major component of the environmental solution testing program. This test system aims to evaluate the sensitivity of the test species, evaluate comparability between laboratory test results, and classify possible variability sources such as healthy organisms, organism batches, laboratory water, and food quality changes. For the continuous assessment of sensitivity and accuracy of laboratory toxicity tests, control charts are often used. The cumulative sum (CUSUM) charting structure is a popular monitoring tool that is often used due to its outstanding ability in recognizing small‐to‐moderate unusual changes in process parameters. The CUSUM control charts have now been widely accepted/applied for monitoring the process dispersion under the assumption that the process does not have outliers. In practice, most of the processes have outliers. Therefore, the use of the interquartile range (IQR) as a plotting statistic is an attractive choice for dispersion monitoring. Hence, in this study, the CUSUM control chart with auxiliary information‐based IQR estimators is proposed and employed to monitor dispersion or variation in the variable LC50 by using quantitative structure–activity relationship (QSAR) aquatic toxicity data set. The proposed IQR‐CUSUM charts are compared to its counterparts by using a variety of main output run‐length functions, such as average run length (ARL), standard deviation of run length (SDRL), and extra quadratic loss (EQL), derived from the distribution of run‐length. Further, results revealed that the proposed CUSUM charts have better detection ability as compared to existing counterpart charts, both in theoretical and practical considerations.

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“…Ahmad et al 24 investigated a set of variance control charts based on one and two auxiliary variables. Riaz 25 proposed a set of Shewhart‐type interquartile range dispersion charts using supplemental information for monitoring process dispersion. Haq et al 26 proposed a synthetic exponentially weighted moving average control chart for monitoring process dispersion.…”

Section: Introductionmentioning

confidence: 99%

Auxiliary‐information‐based efficient variability control charts for Phase I of SPC

Adegoke

2020

Quality & Reliability Eng

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The inclusion of correlated auxiliary variables into the monitoring scheme of quality characteristic of interest has gained notable attention in recent statistical process control (SPC) literature. Several authors have investigated the use of a correlated auxiliary variable for efficient monitoring of variability in Phase II of SPC. This phase is generally used to detect any shifts in the expected behavior of the process parameters which are often estimated from the historical in-control process in Phase I. However, no study has investigated the performance of auxiliary-based variability charts in Phase I of SPC. Here, we propose auxiliary-based dispersion control charts in Phase I of SPC. The auxiliary information is considered in the forms of regression, ratio, exponential, and power-ratio forms. The performance of the variability charts is evaluated and compared using probability to signal as a performance measure. An illustrative example is also provided to show the application of the charts. This study will provide practitioners with appropriate recommendations on the choice of dispersion charts for Phase I analysis.

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On Enhanced Interquartile Range Charting for Process Dispersion

Cited by 19 publications

References 40 publications

A New Maximum EWMA Control Chart for Simultaneously Monitoring Process Mean and Dispersion Using Auxiliary Information

A New Maximum EWMA Control Chart for Simultaneously Monitoring Process Mean and Dispersion Using Auxiliary Information

IQR CUSUM charts: An efficient approach for monitoring variations in aquatic toxicity

Auxiliary‐information‐based efficient variability control charts for Phase I of SPC

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