The general model

Run-sum control charts for monitoring the coefficient of variation

This article proposes a CV chart by using the variable sample size and sampling interval (VSSI) feature to improve the performance of the basic CV chart, for detecting small and moderate shifts in the CV. The proposed VSSI CV chart is designed by allowing the sample size and the sampling interval to vary. The VSSI CV chart's statistical performance is measured by using the average time to signal (ATS) and expected average time to signal (EATS) criteria and is compared with that of existing CV charts. The Markov chain approach is employed in the design of the chart.

Monitoring the coefficient of variation using a variable sample size and sampling interval control chart

In recent years, the coefficient of variation (CV) chart is receiving increasing attention in quality control. A number of studies demonstrated that adaptive charts could detect process shifts faster than traditional charts. This paper proposes an EWMA chart with variable sampling interval (VSI) to monitor the CV. Formulas for computing the performance measures of the VSI EWMA-γ2 chart are derived using Markov chain, where γ2 denotes the CV squared. Comparative studies show that the VSI EWMA-γ2 chart significantly outperforms other competing charts. An example using real manufacturing data shows that the VSI EWMA-γ2 chart performs well in applications.

Monitoring the Coefficient of Variation Using a Variable Sampling Interval EWMA Chart

New adaptive EWMA control charts for monitoring univariate and multivariate coefficient of variation

Monitoring the coefficient of variation using a variable sample size EWMA chart

This paper proposes one-sided coefficient of variation (CV) charts with a linearly covariate error model. The one-sided CV charts are the downward and upward CV charts, which detect decreasing and increasing shifts, respectively. Ignoring the presence of measurement error leads to erroneous conclusions regarding the average run length, especially for small number of measurements per item. However, when measurement error is not ignored in computing the control limits, the performance between the CV charts with and without measurement error is similar. It is recommended to adopt the methods proposed in this paper as a general procedure since in the absence of measurement error, the control limits can be set by letting the size of the measurement error to be zero, whereas traditional methods which assume no measurement error will result in dubious results in the presence of measurement error. Finally, the proposed chart is applied on an illustrative example.

The coefficient of variation chart with measurement error

Optimal designs of the variable sample size and sampling interval X¯ chart when process parameters are estimated

A variable sample size (VSS) scheme directly monitoring the coefficient of variation (CV), instead of monitoring the transformed statistics, is proposed. Optimal chart parameters are computed based on two criteria: (i) minimizing the out-of-control ARL (ARL1) and (ii) minimizing the out-of-control ASS (ASS1). Then the performances are compared between these two criteria. The advantages of the proposed chart over the VSS chart based on the transformed statistics in the existing literature are: the former (i) provides an easier alternative as no transformation is involved and (ii) requires less number of observations to detect a shift when ASS1 is minimized.

Sok Li Lim

Papers

Monitoring the coefficient of variation using a variable sample size EWMA chart

The coefficient of variation chart with measurement error

Optimal designs of the variable sample size and sampling interval X¯ chart when process parameters are estimated

A Direct Procedure for Monitoring the Coefficient of Variation Using a Variable Sample Size Scheme

Optimal design of the synthetic control chart for monitoring the multivariate coefficient of variation