In the present article, a double generally weighted moving average (DGWMA) control chart based on a three-parameter logarithmic transformation is proposed for monitoring the process variability, namely the -DGWMA chart. Monte-Carlo simulations are utilized in order to evaluate the run-length performance of the -DGWMA chart. In addition, a detailed comparative study is conducted to compare the performance of the -DGWMA chart with several well-known memory-type control charts in the literature. The comparisons indicate that the proposed one is more efficient in detecting small shifts, while it is more sensitive in identifying upward shifts in the process variability. A real data example is given to present the implementation of the new -DGWMA chart.
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http://dx.doi.org/10.1080/02664763.2022.2064977 | DOI Listing |
J Appl Stat
April 2022
Department of Mathematics, National Technical University of Athens, Zografou, Greece.
In the present article, a double generally weighted moving average (DGWMA) control chart based on a three-parameter logarithmic transformation is proposed for monitoring the process variability, namely the -DGWMA chart. Monte-Carlo simulations are utilized in order to evaluate the run-length performance of the -DGWMA chart. In addition, a detailed comparative study is conducted to compare the performance of the -DGWMA chart with several well-known memory-type control charts in the literature.
View Article and Find Full Text PDFJ Appl Stat
September 2021
Department of Mathematics, National Technical University of Athens, Zografou, Athens, Greece.
Control charts are widely known quality tools used to detect and control industrial process deviations in Statistical Process Control. In the current paper, we propose a new single memory-type control chart, called the maximum double generally weighted moving average chart (referred as Max-DGWMA), that simultaneously detects shifts in the process mean and/or process dispersion. The run length performance of the proposed Max-DGWMA chart is compared with that of the Max-EWMA, Max-DEWMA, Max-GWMA and SS-DGWMA charts, using time-varying control limits, through Monte-Carlo simulations.
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