EWMA Control Charts with Time-Varying Control Limits and Fast Initial Response

Steiner, Stefan H.

doi:10.1080/00224065.1999.11979899

Cited by 195 publications

(172 citation statements)

References 6 publications

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“…These are the so-called asymptotic control limits. For the difference in performance with the limits based on the exact standard error, the reader is referred to Steiner (1999).…”

Section: Defining the Shewhart Cusum And Ewma Control Chartsmentioning

confidence: 99%

A head-to-head comparative study of the conditional performance of control charts based on estimated parameters

Zwetsloot

Woodall

2016

Quality Engineering

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General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. ABSTRACTImplementation of the Shewhart, CUSUM, and EWMA charts requires estimates of the in-control process parameters. Many researchers have shown that estimation error strongly influences the performance of these charts. However, a given amount of estimation error may differ in effect across charts. Therefore, we perform a pairwise comparison of the effect of estimation error across these charts. We conclude that the Shewhart chart is more strongly affected by estimation error than the CUSUM and EWMA charts. Furthermore, we show that the general belief that the CUSUM and EWMA charts have similar performance no longer holds under estimated parameters.

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“…These are the so-called asymptotic control limits. For the difference in performance with the limits based on the exact standard error, the reader is referred to Steiner (1999).…”

Section: Defining the Shewhart Cusum And Ewma Control Chartsmentioning

confidence: 99%

A head-to-head comparative study of the conditional performance of control charts based on estimated parameters

Zwetsloot

Woodall

2016

Quality Engineering

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“…The EWMA method has been widely studied in the literature, mostly for various shifts in the mean in a stochastic process of normally distributed variables (Roberts (1959); Robinson and Ho (1978); Crowder (1987); Crowder (1989); Lucas and Saccucci (1990); Srivastava and Wu (1993); Chandrasekaran, English, and Disney (1995); Srivastava and Wu (1997); Schone, Schmid, and Knoth (1999); Steiner (1999); Chan and Zhang (2000) and Frisen and Sones son (2001)). However, the EWMA method has also been studied in many other situations.…”

Section: Different Types Of Ewma Methodsmentioning

confidence: 99%

Evaluations of some Exponentially Weighted Moving Average methods

Sonesson¹

2003

Journal of Applied Statistics

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SUMMARYSeveral versions of the EWMA (Exponentially Weighted Moving Average) method for monitoring a process with the aim of detecting a shift in the mean are studied both for the onesided and the two-sided case. The effects of using barriers for the one-sided alarm statistic are also studied. One important issue is the effect of different types of alarm limits. Different measures of evaluation are considered such as the expected delay, the ARLI, the probability of successful detection and the predictive value of an alarm to give a broad picture of the features of the methods. Results are presented both for a fixed ARLO and a fixed probability of a false alarm. The differences highlight the essential problem of how to define comparability between surveillance methods. The results are from a large-scale simulation study. Special attention is given to the effect on the confidence in the final results by the stochastic variation in the calibration of the methods. It appears that important differences from an inferential point of view exist between the one-and two-sided versions of the methods. It is demonstrated that the method, usually considered as a convenient approximation, is to be preferred over the exact version in many respects.

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“…if the process goes out of control at an early stage, the OOC ARL (denoted ARL  ) values may differ substantially depending on the value of the smoothing constant . Steiner (1999) concluded that, in general, exact time-varying control limits are useful when λ is small, say, less than 0.3. For an elaborate discussion on the differences between asymptotic and exact time-varying limits, we refer Knoth (2003Knoth ( , 2005.…”

Section: Statistical Background: Npewma-ex Chartmentioning

confidence: 99%

“…There are certain advantages to doing so. Steiner (1999) compared the run-length characteristics of the EWMA-chart with the exact time-varying control limits to the run-length characteristics of the EWMA-chart with asymptotic control limits. He used the average run-length (ARL) as a performance measure and showed that for an IC process, the IC ARL (denoted ARL 0 ) values of EWMA charts with time-varying control limits are nearly identical to those of EWMA charts with asymptotic control limits.…”

Section: Statistical Background: Npewma-ex Chartmentioning

confidence: 99%

Design and implementation issues for a class of distribution-free Phase II EWMA exceedance control charts

Graham

Mukherjee

Chakraborti

2016

International Journal of Production Research

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Distribution-free (nonparametric) control charts can play an essential role in process monitoring when there is dearth of information about the underlying distribution. In this paper, we study various aspects related to an efficient design and execution of a class of nonparametric Phase II exponentially weighted moving average (denoted by NPEWMA) charts based on exceedance statistics. The choice of the Phase I (reference) sample order statistic used in the design of the control chart is investigated. We use the exact time-varying control limits and the median run-length as the metric in an in-depth performance study. Based on the performance of the chart, we outline implementation strategies and make recommendations for selecting this order statistic from a practical point of view and provide illustrations with a dataset. We conclude with a summary and some remarks.

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EWMA Control Charts with Time-Varying Control Limits and Fast Initial Response

Cited by 195 publications

References 6 publications

A head-to-head comparative study of the conditional performance of control charts based on estimated parameters

A head-to-head comparative study of the conditional performance of control charts based on estimated parameters

Evaluations of some Exponentially Weighted Moving Average methods

Design and implementation issues for a class of distribution-free Phase II EWMA exceedance control charts

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