Developing Sampling Plans Using HEWMA Statistic

2016

A new hybrid exponentially weighted moving average (HEWMA) control chart has been proposed in the literature for efficiently monitoring the process mean. In that paper, the computed variance of the HEWMA statistic was, unfortunately, not correct! In this discussion, the correct variance of the HEWMA statistic is given, and the run length characteristics of the HEWMA control chart are studied and explored. It is noticed that not only the superiority of the HEWMA control chart remains over the existing (considered before) charts but also the new results based on the corrected control limits are more profound and reflective.

Section: Properties Of the Hybrid Exponentially Weighted Moving Averamentioning

confidence: 99%

A New Hybrid Exponentially Weighted Moving Average Control Chart for Monitoring Process Mean: Discussion

2016

“…using the EWMA statistic. In similar vein, Arif et al 7 . proposed a hybrid (double) EWMA (HEWMA) statistic‐based sampling plan, where the EWMA is a special case of the HEWMA.…”

Section: Introductionmentioning

confidence: 99%

A misuse of the EWMA‐type statistic in acceptance sampling applications

Woodall

2022

“…The HEWMA chart encompasses both the classical Shewhart and EWMA charts. For more works on the GWMA and HEWMA charts, see previous studies …”

Section: Introductionmentioning

confidence: 99%

“…For more works on the GWMA and HEWMA charts, see previous studies. [8][9][10][11][12][13][14][15] The classical Shewhart R and S charts are used to monitor the changes in the process dispersion. As the Shewhart dispersion charts are not sensitive against the small shifts, both the CUSUM and EWMA charts have had considerable attention in the construction of improved dispersion charts.…”

Section: Introductionmentioning

confidence: 99%

New memory‐type dispersion control charts

Ali

2017

The exponentially weighted moving average (EWMA) control chart is a memory‐type process monitoring tool that is frequently used to monitor small and moderate disturbances in the process mean and/or process dispersion. In this study, we propose 2 new memory‐type control charts for monitoring changes in the process dispersion, namely, the generally weighted moving average and the hybrid EWMA charts. We use Monte Carlo simulations to compute the run length profiles of the proposed control charts. The run length comparisons of the proposed and existing charts reveal that the generally weighted moving average and hybrid EWMA charts provide better protection than the existing EWMA chart when detecting small to moderate shifts in the process dispersion. An illustrative dataset is also used to show the superiority of the proposed charts over the existing chart.