Monitoring the Coefficient of Variation Using Control Charts with Run Rules

Castagliola, Philippe; Achouri, A; Taleb, Hassen; Celano, Giovanni; Psarakis, Stelios

doi:10.1080/16843703.2013.11673309

Cited by 89 publications

(88 citation statements)

References 2 publications

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“…In addition, Castagliola et al (2013a) evaluated an adaptive Shewhart control chart implementing variable sampling interval strategy to monitor the process CV. Castagliola et al (2013b) proposed a Shewhart chart with supplementary run rules to monitor the CV. However, as they pointed out, the run rules charts for monitoring the CV does not outperform more advanced strategies like the chart proposed by Castagliola et al (2011) or the synthetic chart proposed by Calzada and Scariano (2013).…”

Section: Introductionmentioning

confidence: 99%

A new exponentially weighted moving average control chart for monitoring the coefficient of variation

Zhang

Chen

et al. 2014

Computers & Industrial Engineering

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Section: Introductionmentioning

confidence: 99%

A new exponentially weighted moving average control chart for monitoring the coefficient of variation

Zhang

Chen

et al. 2014

Computers & Industrial Engineering

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“…More recently, Castagliola et al [3] suggested a new method to monitor the CV by means of two one-sided EWMA charts of the CV squared. Calzada and Scariano [4] suggested a synthetic control chart (denoted SynCV) for monitoring the CV, and Castagliola et al [5][6][7] proposed alternative approaches to monitor the CV based on Run Rules, Variable Sampling Interval (VSI), and Variable Sample Size (VSS) strategies, respectively.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the coefficient of variation using a variable sample size control chart in short production runs

Amdouni

Castagliola

Taleb

et al. 2015

Int J Adv Manuf Technol

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Monitoring the coefficient of variation (CV) is an effective approach to monitor a process when both the process mean and the standard deviation are not constant but, nevertheless, proportional. Until now, few contributions have investigated the monitoring of the CV for short production runs. This paper proposes an adaptive Shewhart control chart implementing a variable sample size (VSS) strategy in order to monitor the coefficient of variation in a short production run context. Formulas for the truncated average run length are derived. Moreover, a comparison is performed with a Fixed Sampling Rate Shewhart chart for the CV in order to evaluate the performance of each chart in a short run context. An example illustrates the use of this chart on real data.

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“…In quality 10 control engineering, some authors (see e.g. [5]) have proposed various control charts, i.e. statistical tools used to determine whether a manufacturing process is in-control, based on the coefficient of variation.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, in these cases, the probability that expression (11) takes values close to zero is higher and the estimator can thus take arbitrary large values 385 under these settings. The two parametric corrections based on the normality assumption,V (2) n andV (5) n , lead to an effective reduction in bias and their MSE's remain similar to those of V n except when γ = 2 and n/p is small. The two non-parametric corrections, i.e.…”

mentioning

confidence: 99%

“…As a reminder, V n denotes the classical estimator, while V 1 n uses an unbiased estimator of γ −2 under normality. All other estimators, based on the same principle, use an estimator of the bias obtained from the expectation-curve under normality forV (2) n , by jackknife forV (3) n , by bootstrap forV (4) n and by inversion of the expectation-curve under normality forV (5) n . We can observe that all the obtained 345 corrected estimates are indeed higher than the classical estimates.…”

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confidence: 99%

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Distribution under elliptical symmetry of a distance-based multivariate coefficient of variation

2016

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In the univariate setting, the coefficient of variation is widely used to measure the relative dispersion of a random variable with respect to its mean. Several extensions of the univariate coefficient of variation to the multivariate setting have been introduced in the literature. In this paper, we focus on a distancebased multivariate coefficient of variation. First, some real examples are discussed to motivate the use of the considered multivariate dispersion measure. Then, the asymptotic distribution of several estimators is analyzed under elliptical symmetry and used to construct approximate parametric confidence intervals that are compared with non-parametric intervals in a simulation study. Under normality, the exact distribution of the classical estimator is derived. As this natural estimator is biased, some bias corrections are proposed and compared by means of simulations.

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Monitoring the Coefficient of Variation Using Control Charts with Run Rules

Cited by 89 publications

References 2 publications

A new exponentially weighted moving average control chart for monitoring the coefficient of variation

A new exponentially weighted moving average control chart for monitoring the coefficient of variation

Monitoring the coefficient of variation using a variable sample size control chart in short production runs

Distribution under elliptical symmetry of a distance-based multivariate coefficient of variation

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