The effect of autocorrelation on the retrospective<i>X</i>-chart

Maragah, Hazem D.; Woodall, William H.

doi:10.1080/00949659208811363

Cited by 107 publications

(39 citation statements)

References 10 publications

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“…Alwan and Roberts (1988) proposed a general approach to monitor residuals of Univariate auto correlated time series where the systematic patterns are filtered out and the special changes are more exposed. Other studies include Montgomery and Friedman (1989), Harris and Ross (1991), Montgomery and Mastrangelo (1991), Maragah and Woodall (1992), Wardell, Moskowitz and Plante (1994), Lu and Reynolds (1999), West, Delana and Jarrett (2002) and West and Jarrett (2004), English and Sastri (1990), Pan and Jarrett (2004) suggested state space methodology for the control of auto correlated process. Further, additional technologies implemented by Testik (2005), Yang and Rahim (2005) and Yeh, Huang and Wu (2004) provide newer methods for enabling better MPC methods.…”

Section: Multivariate Quality Control (Mqc)mentioning

confidence: 99%

The quality movement in hospital care

Jarrett

2013

Qual Quant

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The purpose is to introduce the demand for the quality movement practice in hospital care. . We show both the need and application of quality monitoring, especially the need monitoring activities having auto correlated data flows of which there are many in the hospital environment. The goal is to control the flow of quality care data in the dynamic behavior of these systems of acre in hospitals. These monitoring systems are designed to control and improve changes in the hospital care environment. AbstractThe purpose is to introduce the demand for the quality movement practice in hospital care. . We show both the need and application of quality monitoring, especially the need monitoring activities having auto correlated data flows of which there are many in the hospital environment. The goal is to control the flow of quality care data in the dynamic behavior of these systems of acre in hospitals. These monitoring systems are designed to control and improve changes in the hospital care environment.

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Section: Multivariate Quality Control (Mqc)mentioning

confidence: 99%

The quality movement in hospital care

Jarrett

2013

Qual Quant

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“…The study was restricted to a class of stationary ARMA models, AR (1), AR (2), and MA (1). Maragah et al (1992) also considered the capability of the Shewhart control chart but analyzed just the AR (1) and MA (1) models. These two studies came to the same conclusion -that even mild levels of autocorrelation can dramatically affect the Shewhart chart's ability to correctly separate assignable causes from random causes.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of Weibull data and autocorrelation on the performance of the Shewhart and exponentially weighted moving average control charts

Black¹,

Smith²,

Wells³

2011

IJIEC

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Many real-world processes generate autocorrelated and/or Weibull data. In such cases, the independence and/or normality assumptions underlying the Shewhart and EWMA control charts are invalid. Although data transformations exist, such tools would not normally be understood or employed by naive practitioners. Thus, the question arises, "What are the effects on robustness whenever these charts are used in such applications?" Consequently, this paper examines and compares the performance of these two control charts when the problem (the model) is subjected to autocorrelated and/or Weibull data. A variety of conditions are investigated related to the magnitudes of various parameters related to the process shift, the autocorrelation coefficient and the Weibull shape parameter. Results indicate that the EWMA chart outperforms the Shewhart in 62% of the cases, particularly those cases with low to moderate autocorrelation effects. The Shewhart chart outperforms the EWMA chart in 35% of the cases, particularly those cases with high autocorrelation and zero or high process shift effects.

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“…Maragah and Woodall [3] provided results on the effect of autocorrelation on the performance of the Shewhart individuals control chart. Alwan and Roberts [4] found that more than 70% of the studied processes subject to change detection in quality control are autocorrelated.…”

Section: Introductionmentioning

confidence: 99%

Cost comparisons of modifiedX̅chart for autocorrelated observations

Prajapati

2016

Int. J. Metrol. Qual. Eng.

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Abstract. This paper presents the economic design of modified X chart for autocorrelated data and comparison with the economic design of Shewhart's X chart. An attempt has been made to counter autocorrelation by designing the modified X chart; as the cost of operating a process control system is an important element in the economic design of control charts. The economic designs of both modified and Shewhart X charts for autocorrelated observations are presented; using Lorenzen-Vance [1] cost model. The modified X chart is based upon sum of chi-squares and has simplicity like Shewhart X chart but more efficient than standard chart. Two sets of parameters suggested by Montgomery [2] are used to determine the optimal sampling intervals (h) and expected costs per hour. It shows that sampling interval (h) and expected cost/hour (C) depend upon various parameters of chart, used in this model. The expected costs/hour (C) for both the charts are computed for sample sizes of 2 and 4 at the various levels of correlation and process shifts in mean. It is concluded that at each level of correlation and shift in the process mean, the expected costs/hour of the modified X chart are lower than the Shewhart chart.

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The effect of autocorrelation on the retrospectiveX-chart

Cited by 107 publications

References 10 publications

The quality movement in hospital care

The quality movement in hospital care

The impact of Weibull data and autocorrelation on the performance of the Shewhart and exponentially weighted moving average control charts

Cost comparisons of modifiedX̅chart for autocorrelated observations

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