A direct procedure for monitoring the coefficient of variation using a variable sample size scheme

Yeong, Wai Chung; Khoo, Michael B. C.; Lim, Sok Li; Lee, Ming Ha

doi:10.1080/03610918.2015.1109659

Cited by 29 publications

(20 citation statements)

References 4 publications

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“…The VSS CV chart was proposed by [20] and [21]. [20] monitored a transformed function of the CV, while [21] monitored the CV directly. The VSS CV chart by [21] is adopted in this paper.…”

Section: Variable Sample Size (Vss) Coefficient Of Variation (Cv) Chartmentioning

confidence: 99%

“…[20] monitored a transformed function of the CV, while [21] monitored the CV directly. The VSS CV chart by [21] is adopted in this paper. Like the VSI CV chart, more stringent control is adopted when there are signs that the process is going out-of-control.…”

Section: Variable Sample Size (Vss) Coefficient Of Variation (Cv) Chartmentioning

confidence: 99%

“…To evaluate the ATS of the VSS CV chart, a Markov chain approach is adopted. By defining the first and second states as the central and warning regions, respectively, while the third state is defined as the out-of-control region, [21] has shown that the transition probability matrix (tpm) P has the following structure:…”

Section: Variable Sample Size (Vss) Coefficient Of Variation (Cv) Chartmentioning

confidence: 99%

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A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

Lim¹,

Yeong²,

Khoo³

et al. 2018

World Congress on Mechanical, Chemical, and Material Engineering

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An efficient way to perform quality monitoring is important for a lot of industries, particularly in the manufacturing industry. Control charts are one of the important tools to monitor quality. Recently, control charts monitoring the coefficient of variation (CV) are proposed to monitor processes which do not have a constant mean and a standard deviation which changes with the mean. These processes cannot be monitored by standard control charts which monitor the mean and/or standard deviation. The Variable Sampling Interval (VSI) and Variable Sample Size (VSS) CV charts are two recent charts proposed to improve the detection ability of CV charts. This paper compares the performance between these two charts in terms of the Average Time to Signal (ATS). The comparison shows that the VSS CV chart shows better performance when either a small sample size is taken or when a small shift in the CV needs to be detected. Alternatively, the VSI CV chart outperforms the VSS CV chart. This paper will enable practitioners to know the conditions where the VSS CV chart outperforms the VSI CV chart, and vice versa. Hence, practitioners will be able to decide whether it is better to monitor their processes with a VSI or VSS scheme.

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“…The VSS CV chart was proposed by [20] and [21]. [20] monitored a transformed function of the CV, while [21] monitored the CV directly. The VSS CV chart by [21] is adopted in this paper.…”

Section: Variable Sample Size (Vss) Coefficient Of Variation (Cv) Chartmentioning

confidence: 99%

Section: Variable Sample Size (Vss) Coefficient Of Variation (Cv) Chartmentioning

confidence: 99%

Section: Variable Sample Size (Vss) Coefficient Of Variation (Cv) Chartmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

Lim¹,

Yeong²,

Khoo³

et al. 2018

World Congress on Mechanical, Chemical, and Material Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the CV is a relative measure of dispersion, a control chart for the CV allows to detect the unexpected change in the ratio between µ and σ. Several control charts using different methods such as Run-sum [1], CUSUM [2], synthetic [3,4], EWMA [5,6] have been discussed in literature to monitor the coefficient of variation of a normal distribution [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

One-sided synthetic control charts for monitoring the multivariate coefficient of variation

Nguyen

Tran

Castagliola

et al. 2019

Journal of Statistical Computation and Simulation

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Shewhart's type control charts for monitoring the Multivariate Coefficient of Variation (MCV) have recently been proposed in order to monitor the relative variability compared with the mean. These approaches are known to be rather slow in the detection of small or moderate process shifts. In this paper, in order to improve the detection efficiency, two one-sided Synthetic charts for the MCV are proposed. A Markov chain method is used to evaluate the statistical performance of the proposed charts. Furthermore, computational experiments reveal that the proposed control charts outperform the Shewhart MCV control chart in terms of the average run length to detect an out-of-control state. Finally, the implementation of the proposed chart is illustrated with an example using steel sleeves data.

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“…Castagliola et al evaluated the performance of the VSS chart that monitors the coefficient of variation (CV) of a process. Additionally, Yeong et al presented the VSS chart that monitors the CV directly instead of the transformed statistics employed by Castagliola et al Amiri et al extended the VSS approach to the exponentially weighted moving average (EWMA) control chart. More research works on the VSS control charts can be found in Mahadik, Wu and Luo, Noorossana and Maryam, Mahadik, Cheng and Wang, Aslam et al, Zhou and Lian, Nyau et al, and Lee .…”

Section: Introductionmentioning

confidence: 99%

Hotelling's T² control charts with fixed and variable sample sizes for monitoring short production runs

Chong

Khoo

Haq

et al. 2018

Quality & Reliability Eng

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Short production runs are common in enterprises that require a high degree of flexibility and variety in manufacturing processes. To date, past research on short production runs has little focus on the multivariate control charts. In view of this, fixed sample size (FSS) and variable sample size (VSS) Hotelling's T2 charts are designed to monitor the process mean when the production horizon is finite. Optimal parameters to minimize the out‐of‐control (1) truncated average run length (TARL) and (2) expected TARL (ETARL) are provided such that the in‐control TARL is equal to the number of inspections (say I). The numerical study considers the run length performances of the FSS and VSS T2 short‐run charts for both known and unknown shift sizes. The VSS T2 short‐run chart performs well in swiftly detecting various mean shifts in comparison with the FSS T2 short‐run chart. Additionally, the VSS T2 short‐run chart is superior to the FSS T2 short‐run chart, in terms of the truncated standard deviation of the run length, expected truncated standard deviation of the run length, probability that the chart signals an alarm within the I inspections, ie, P(I) and expected P(I). A case study on the impurity profile of a crystalline drug substance illustrates the implementation of the VSS T2 short‐run chart.

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A direct procedure for monitoring the coefficient of variation using a variable sample size scheme

Cited by 29 publications

References 4 publications

A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

One-sided synthetic control charts for monitoring the multivariate coefficient of variation

Hotelling's T² control charts with fixed and variable sample sizes for monitoring short production runs

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A direct procedure for monitoring the coefficient of variation using a variable sample size scheme

Cited by 29 publications

References 4 publications

A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

One-sided synthetic control charts for monitoring the multivariate coefficient of variation

Hotelling's T2 control charts with fixed and variable sample sizes for monitoring short production runs

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Product

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Hotelling's T² control charts with fixed and variable sample sizes for monitoring short production runs