New GWMA‐CUSUM control chart for monitoring the process dispersion

The cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been widely accepted because of their fantastic speed in identifying small‐to‐moderate unusual variations in the process parameter(s). Recently, a new CUSUM chart has been proposed that uses the EWMA stat...

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Bibliographic Details
Published in:Quality and reliability engineering international 2018-10, Vol.34 (6), p.997-1028
Main Authors: Douglas, Montgomery, Ali, Rizwan, Haq, Abdul
Format: Article
Language:English
Subjects:
average run length
Computer simulation
control chart
Control charts
cumulative sum
CUSUM charts
Dispersion
exponentially weighted moving average
Monitoring
Parameter identification
Process controls
process dispersion
Process parameters
statistical process control
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Summary:The cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been widely accepted because of their fantastic speed in identifying small‐to‐moderate unusual variations in the process parameter(s). Recently, a new CUSUM chart has been proposed that uses the EWMA statistic, called the CS‐EWMA chart, for monitoring the process variability. On similar lines, in order to further improve the detection ability of the CS‐EWMA chart, we propose a CUSUM chart using the generally weighted moving average (GWMA) statistic, named the GWMA‐CUSUM chart, for monitoring the process dispersion. Monte Carlo simulations are used to compute the run length profiles of the GWMA‐CUSUM chart. On the basis of the run length comparisons, it turns out that the GWMA‐CUSUM chart outperforms the CUSUM and CS‐EWMA charts when identifying small variations in the process variability. A simulated dataset is also used to explain the working and implementation of the CS‐EWMA and GWMA‐CUSUM charts.
ISSN:0748-8017
1099-1638
DOI:10.1002/qre.2304