Improving the Performance of Exponentially Weighted Moving Average Control Charts

Abujiya, Mu’azu Ramat; Lee, Muhammad Hisyam; Riaz, Muhammad

doi:10.1002/qre.1509

Cited by 48 publications

(34 citation statements)

References 24 publications

(61 reference statements)

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“…Monte Carlo simulations in the R language are programmed followed by Abujiya et al for monitoring the process variation of the lognormal distribution using RS scheme for both cases of standard deviation ( σ > 1 & σ < 1) to find out the ARLs and SDRLs. Here, the process reported in‐control with shift c = 1.…”

Section: Designing Of the Proposed Control Chartmentioning

confidence: 99%

A control chart for monitoring the lognormal process variation using repetitive sampling

Shaheen

Azam

Aslam

2020

Quality & Reliability Eng

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Control charts are developed to make the specific quality measures for a successful production process and follow normal distribution behaviors. But some real‐life practices do not match such practices and exhibit some positively skewed behavior like lognormal distribution. The present study has considered this situation and proposed a monitoring control chart based on lognormal process variation using a repetitive sampling scheme. This concept proved better for detecting shifts as quickly as possible, and compared with the existing concept, results are elaborated through extensive tables. The average run lengths and standard deviations of the run lengths are being used as a performance evaluation measures and computed by using Monte Carlo simulations performed in R language. A real‐life situation has been discussed in the example section to strengthen the proposed control chart concept in a real‐life situation.

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Section: Designing Of the Proposed Control Chartmentioning

confidence: 99%

A control chart for monitoring the lognormal process variation using repetitive sampling

Shaheen

Azam

Aslam

2020

Quality & Reliability Eng

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“…The Monte Carlo simulation procedure is as follows (Abujiya, Lee, & Riaz, 2014): 3.1. (Sample mean and variance of control statistic) 3.1.1 Generate a random sample of size n at each subgroup (we used a sample of size n = 5, 10 or 15 as given in Tables 1, 3 and 5) from a selected distribution having the specified parameters for in-control process.…”

Section: Performance Evaluationmentioning

confidence: 99%

Dispersion chart for some popular distributions under repetitive sampling

Ahmad

Aslam

Arif

2016

JAMDSM

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“…For some details on the CUSUM and the EWMA charts based on RSS schemes, see Refs. and references cited therein.…”

Section: Introductionmentioning

confidence: 99%

New Synthetic Control Charts for Monitoring Process Mean and Process Dispersion

Haq

Brown

Moltchanova

2014

Quality & Reliability Eng

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A statistical quality control chart is widely recognized as a potentially powerful tool that is frequently used in many manufacturing and service industries to monitor the quality of the product or manufacturing processes. In this paper, we propose new synthetic control charts for monitoring the process mean and the process dispersion. The proposed synthetic charts are based on ranked set sampling (RSS), median RSS (MRSS), and ordered RSS (ORSS) schemes, named synthetic‐RSS, synthetic‐MRSS, and synthetic‐ORSS charts, respectively. Average run lengths are used to evaluate the performances of the control charts. It is found that the synthetic‐RSS and synthetic‐MRSS mean charts perform uniformly better than the Shewhart mean chart based on simple random sampling (Shewhart‐SRS), synthetic‐SRS, double sampling‐SRS, Shewhart‐RSS, and Shewhart‐MRSS mean charts. The proposed synthetic charts generally outperform the exponentially weighted moving average (EWMA) chart based on SRS in the detection of large mean shifts. We also compare the performance of the synthetic‐ORSS dispersion chart with the existing powerful dispersion charts. It turns out that the synthetic‐ORSS chart also performs uniformly better than the Shewhart‐R, Shewhart‐S, synthetic‐R, synthetic‐S, synthetic‐D, cumulative sum (CUSUM) ln S2, CUSUM‐R, CUSUM‐S, EWMA‐ln S2, and change point CUSUM charts for detecting increases in the process dispersion. A similar trend is observed when the proposed synthetic charts are constructed under imperfect RSS schemes. Illustrative examples are used to demonstrate the implementation of the proposed synthetic charts. Copyright © 2014 John Wiley & Sons, Ltd.

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Improving the Performance of Exponentially Weighted Moving Average Control Charts

Cited by 48 publications

References 24 publications

A control chart for monitoring the lognormal process variation using repetitive sampling

A control chart for monitoring the lognormal process variation using repetitive sampling

Dispersion chart for some popular distributions under repetitive sampling

New Synthetic Control Charts for Monitoring Process Mean and Process Dispersion

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