Design and analysis of exponentially weighted moving average control charts for monitoring the variability of log‐normal processes with estimated parameters

Abstract

In statistical process control (SPC), the control chart is a quite popular technique to monitor the process efficacy. From a statistical point of view, the control chart is considered superior if it has an effective structure withholding property of the resistance against infrequent situations in a practical environment. The current study is designed for the same purpose for observing the dispersion parameter of log‐normal distribution by using the structure of an exponentially weighted moving average (EWMA) chart. For the extensive study, EWMA range, EWMA standard deviation, EWMA Qn,${Q_n},$ EWMA Sn,${S_n},$ EWMA mean absolute deviation, and EWMA transform standard deviation control charts are proposed. Properties of the run‐length profile of the proposed designed structures based on different existing estimators as well as a newly transformed dispersion estimator for log‐normal standard deviation are evaluated. The results indicate that the newly developed scheme outperforms the competitors when the dispersion parameter of the log‐normal distribution attains a large value. A real‐life application is also provided to validate how the developed design can be used in practice.

Joint monitoring of mean and variance using Max-EWMA control chart under lognormal process with application to engine oil data

“…Their findings indicated that the proposed S-chart exhibited superior performance as compared to alternative methods. Akhtar et al 20 developed an EWMA chart customized for the lognormal process. Iqbal et al 21 introduced a control chart for monitoring the process mean and variability under the Bayesian Approach.…”

Enhanced Dispersion Monitoring Structures Based on Modified Successive Sampling: Application to Fertilizer Production Process

“…A double generally weighted moving average (GWMA) control chart for monitoring dispersion parameters was designed by Alevizakos et al [25]. Akhtar et al [26] evaluated the EWMA control chart by using log-normal distributions with estimated parameters to monitor process variability. A GWMA maximum chart for joint monitoring was designed by Chatterjee et al [27].…”

The Performance of S Control Charts for the Lognormal Distribution with Estimated Parameters

“…Shaheen et al [23] developed a control chart for monitoring the lognormal process variation using repetitive sampling. Akhtar et al [24] designed an exponentially weighted moving average (EWMA) chart for the lognormal process. In this study, we discuss three S-charts, which include the Shewhart S-chart, the MAD control chart, and the lognormal S control chart, on the AARL and SDARL performance.…”