Discussion

Woodall, William H.; Maragah, Hazem D.

doi:10.1080/00401706.1990.10484585

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…In this procedure both EWMA and Shewhart control statistics are plotted; if either statistic exceeds its own control limits an out-of-control signal is given. Woodall and Maragah (1990) further emphasized that a Shewhart chart should always be used in conjunction with the EWMA chart, and Klein (1996) showed that the combined (or 'composite', which is used by the author) Shewhart-EWMA control chart has better Average Run Length (ARL) performance than the Shewhart chart supplemented with run rules.…”

Section: Introductionmentioning

confidence: 97%

Composite EWMA Control Charts

Zhang

Govindaraju

Lai

2004

Communications in Statistics - Simulation and Computation

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We propose a new charting procedure, combining two or more Exponentially Weighted Moving Averages (EWMAs), which we call a Composite EWMA (CEWMA) Control Chart. In the case of two EWMAs, the CEWMA chart corresponds to the usual combined Shewhart-EWMA chart, although our plotting procedure remain, we feel, an improvement in application and interpretation. When three EWMAs are combined, the composite chart improves on the average run length performance of the combined Shewhart-EWMA chart. We also briefly discuss the combination of more than three EWMAs.

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Section: Introductionmentioning

confidence: 97%

Composite EWMA Control Charts

Zhang

Govindaraju

Lai

2004

Communications in Statistics - Simulation and Computation

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“…Lucas and Saccucci advocated the use of the EWMA scheme with Shewhart control limits to overcome the sudden changes in a process. Woodall and Maragah, also recommended the combination of control limits for the memory and memory‐less control charts to monitor small and large shifts, simultaneously. Reynolds also studied an adaptive‐EWMA control chart for efficient process monitoring.…”

Section: Introductionmentioning

confidence: 99%

An adaptive approach to EWMA dispersion chart using Huber and Tukey functions

Zaman

Lee

Riaz

et al. 2019

Quality & Reliability Eng

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Random causes are vital part of every process in manufacturing and nonmanufacturing environments, and these do not affect the product features. Special causes, on the other hand, come because of some burden(s) in a process and requires special attention; otherwise, it ruins the products excellence. Special causes are categorized into small, moderate, and large shifts and are handled by statistical quality control charts. The Shewhart control chart is well known for large shifts, while the cumulative sum and exponentially weighted moving average are more effective in detecting small to moderate shifts. However, in practice, many processes require the simultaneous monitoring of both the small to the large shifts. In this study, we have designed an adaptive EWMA for dispersion parameter in connection with Huber and Tukey's bisquare functions. The performance measures used in this study include average run length, extra quadratic loss, relative average run length, and performancecomparison index. We have observed that the study proposals are good competitors to the other counter parts for an efficient monitoring of shifts of varying amounts. An illustrative example using real data is given to demonstrate the implementation of the study proposal.

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Discussion

Lucas

Saccucci

1990

Technometrics

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Discussion

Cited by 4 publications

References 5 publications

Composite EWMA Control Charts

Composite EWMA Control Charts

An adaptive approach to EWMA dispersion chart using Huber and Tukey functions

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