Development of CV Control Chart Using EWMA Technique

By E. Hong, C. Kang, Jae-Won Paik, 강해운. 2008

82 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/95dab0e9452f69889cfef2dd44f79c5d8deea7b9

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Citations in the corpus

Monitoring the Coefficient of Variation Using a Variable Sampling Interval EWMA Chart (2017, background): “Hong et al. (2008) proposed an exponentially weighted moving average (EWMA) CV control chart.”
Monitoring the Coefficient of Variation Using EWMA Charts (2011, methodology): “…to make a fair comparison between the two charts, we have computed the outof-control ARLs corresponding to one-sided, fixedlimits versions of the original Hong et al. (2008) chart (using the same method as for the EWMA-γ2 chart but using γ instead of γ2) for the same in-control value ARL0 = 370.4.”
Monitoring the Coefficient of Variation Using Control Charts with Run Rules (2013, methodology): “[13] suggested to use the approximations proposed by Reh and Scheffler [28], i.”
One-Sided Shewhart-type Charts for Monitoring the Coefficient of Variation in Short Production Runs (2015, methodology): “[11], were the first to propose an EWMA-CV (Exponentially Weighted Moving Average) control chart in order to improve the CV chart proposed by Kang et al.”
Monitoring the coefficient of variation using a variable sample size control chart (2015, background): “[10] were the first to propose an exponentially weighted moving average (EWMA) control chart in order to improve the CV chart proposed by [12] and detect small shifts more efficiently.”
Monitoring the coefficient of variation using a variable sample size control chart in short production runs (2015, background): “[2] were the first to propose an ExponentiallyWeightedMoving Average CV (EWMA-CV) control chart to quickly detect small shifts.”