The effect of continuously updating control chart limits on control chart performance

Huberts, Leo C.E.; Schoonhoven, Marit; Does, Ronald J. M. M.

doi:10.1002/qre.2448

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…The control chart is a vital tool in statistical process control (SPC), and it is often viewed as an effective process monitoring technique in industries to detect the presence of assignable causes. This can be shown through a variety of recent publications, such as those by Aldosari et al, Chukhrova and Johannssen, Huberts et al, and Tran et al, to name a few. In many real‐life applications, a simultaneous monitoring of at least two related quality variables is necessary.…”

Section: Introductionmentioning

confidence: 91%

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

Chew

Khoo

Khaw

et al. 2019

Quality & Reliability Eng

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An efficient process monitoring system is important for achieving sustainable manufacturing. The control charting technique is one of the most effective techniques to monitor process quality. In certain processes where the process mean and variance are not independent of one another, the coefficient of variation (CV), which measures the ratio of the standard deviation to the mean, should be monitored. In line with industrial settings, where at least two or more variables are monitored simultaneously in most processes, this paper proposes a variable parameter (VP) chart to monitor the multivariate CV (MCV). Formulae and algorithms to optimize the various performance measures are discussed. The proposed VP MCV chart is designed based on a Markov chain approach. The performance comparison shows that the proposed VP MCV chart prevails over the existing MCV charts, in terms of the average time to signal (ATS), standard deviation of the time of signal (SDTS), and expected average time to signal (EATS) criteria. An example is presented to illustrate the implementation of the proposed VP MCV chart.

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Section: Introductionmentioning

confidence: 91%

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

Chew

Khoo

Khaw

et al. 2019

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…However, a pitfall of such self‐starting methods is the possibility of incorporating out‐of‐control observations into the parameter estimates. Huberts et al 60 investigated the effect of continuously updating control chart limits on the control chart performance under various out‐of‐control scenarios as well. They find indeed that updating of parameters should be done with caution.…”

Section: Other Developmentsmentioning

confidence: 99%

On the design of control charts with guaranteed conditional performance under estimated parameters

Does

Goedhart

Woodall

2020

Quality & Reliability Eng

Self Cite

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When designing control charts the in‐control parameters are unknown, so the control limits have to be estimated using a Phase I reference sample. To evaluate the in‐control performance of control charts in the monitoring phase (Phase II), two performance indicators are most commonly used: the average run length (ARL) or the false alarm rate (FAR). However, these quantities will vary across practitioners due to the use of different reference samples in Phase I. This variation is small only for very large amounts of Phase I data, even when the actual distribution of the data is known. In practice, we do not know the distribution of the data, and it has to be estimated, along with its parameters. This means that we have to deal with model error when parametric models are used and stochastic error because we have to estimate the parameters. With these issues in mind, choices have to be made in order to control the performance of control charts. In this paper, we discuss some results with respect to the in‐control guaranteed conditional performance of control charts with estimated parameters for parametric and nonparametric methods. We focus on Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) control charts for monitoring the mean when parameters are estimated.

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“…Huberts at el. [34] introduced the method of continuously updating the control limits of control chart, when data is provided in subgroup form.…”

Section: Significance Of Subgrouping In Control Chartsmentioning

confidence: 99%

On the Performance of Median Based Tukey and Tukey-EWMA Charts Under Rational Subgrouping

et al. 2019

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Control chart (CC) is used to monitor the special causes that arise during the process monitoring.These special causes produce continual shifts in the process parameters that last until it is identified and removed. There is a need for such techniques, which present the true representation of the entire process. Rational subgrouping is an essential concept in Statistical Process Control (SPC) which is seldom overlooked by the practitioner. Hence, most of the manufacturing, engineering, and production processes give output products in the form of batches over smaller intervals of time. The aim of this study is to provide a median based design for Tukey and Tukey-EWMA control charts under subgrouping. It will use the idea of boxplot to monitor the process behavior. This study also provides a brief discussion regarding selecting and forming subgroups from the process data. The performance of the median based Tukey and Tukey-EWMA charts are judged using Average, Median and Standard-Deviation run-length as performance measures. We have considered subgroup sizes of m=1,5 &10 at pre-specified ARL0 equal to 370. To real-life applications of the median based tukey designs are also presented to show their implementation in food manufacturing and hard-bake processes.2

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The effect of continuously updating control chart limits on control chart performance

Cited by 10 publications

References 25 publications

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

On the design of control charts with guaranteed conditional performance under estimated parameters

On the Performance of Median Based Tukey and Tukey-EWMA Charts Under Rational Subgrouping

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