A Variable Sampling Interval EWMA Distribution-Free Control Chart for Monitoring Services Quality

Tran, Phuong Thi Minh; Tran, Kim Phuc; Hương, Trương Thu; Heuchenne, Cédric; Nguyen, Thi Anh Dao; Ngon, Cong

doi:10.1145/3194188.3194191

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…The VSI type of chart is combined with the VSS type of chart based on the Shewhart chart by Khaw et al 10 . Tracking the CV is also done by incorporating VSI into the EWMA chart according to the suggestion of Yeong et al 16 and Tran et al 15 . The authors of the last two aforementioned papers showed that their proposed control charts are actually more effective than others such as Shewhart chart, synthetic chart, EWMA chart, or VSI Shewhart chart.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the coefficient of variation using variable sampling interval CUSUM control charts

Tran

Heuchenne

2020

Journal of Statistical Computation and Simulation

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It is proved that the variable sampling interval (VSI) type charts are a useful adaptive strategy to improve the efficiency of a control chart. In monitoring the coefficient of variation (CV), this strategy has been recently combined with various types of charts, including exponentially weighted moving average (EWMA) and variable sample size (VSS) charts. These combinations lead to desirable control charts with an impressive performance in detecting process shifts. However, in these studies, the authors asserted that adaptive type charts are not competitive to monitor downward shifts in the CV. As a consequence, they willingly omitted the decrease in process shifts. In this paper, we suggest overcoming this downside by designing two one-sided VSI CUSUM control charts for tracking the CV. The performance of the proposed charts is evaluated by the average time to signal (ATS) criterion. The numerical results show that our proposed adaptive type charts are actually efficient charts to monitor downward shifts in the CV.

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Section: Introductionmentioning

confidence: 99%

Monitoring the coefficient of variation using variable sampling interval CUSUM control charts

Tran

Heuchenne

2020

Journal of Statistical Computation and Simulation

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One-sided variable sampling interval EWMA control charts for monitoring the multivariate coefficient of variation in the presence of measurement errors

Nguyen

Giner‐Bosch

Tran

et al. 2021

Int J Adv Manuf Technol

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Online monitoring of the multivariate coefficient of variation (MCV) can be of interest in many real situations in which the dispersion of a multivariate process is meant to remain constant with regards to its position. To this aim, several control charts have been recently proposed in the literature. In this paper, new one-sided adaptive charts to monitor the MCV are proposed. The chart applies a variable sampling interval (VSI) strategy on an exponentially weighted moving average (EWMA) scheme, aiming at benefiting from the known advantages of both approaches. Formulas to optimally determine the parameters of the chart are derived and presented. The proposed chart is shown to outperform competing charts under most circumstances. The presence of the measurement errors is considered to understand their effects on the charts. An illustrative example is also included.

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2021

Journal of Statistical Computation and Simulation

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A Variable Sampling Interval EWMA Distribution-Free Control Chart for Monitoring Services Quality

Cited by 5 publications

References 29 publications

Monitoring the coefficient of variation using variable sampling interval CUSUM control charts

Monitoring the coefficient of variation using variable sampling interval CUSUM control charts

One-sided variable sampling interval EWMA control charts for monitoring the multivariate coefficient of variation in the presence of measurement errors

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