A maximum adaptive exponentially weighted moving average control chart for monitoring process mean and variability. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Haq, Abdul
• Is Part Of: Quality technology & quantitative management. Volume 17:Issue 1(2020)
• Page Start: 16
• Page End: 31
• Publication Date: 2020-01-02
• Subjects: Average run length -- maximum adaptive EWMA -- maximum EWMA -- Monte Carlo simulation -- process mean and dispersion -- statistical process control; Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2018.1530181
• Languages: English
• ISSNs: 1684-3703

Abstract

ABSTRACT: The maximum exponentially weighted moving average (MaxEWMA) chart is widely recognized as an efficient statistical process monitoring tool because of its ability to respond quickly against small-to-moderate shifts in the process parameters. In this article, we propose a maximum adaptive exponentially weighted moving average (MaxAEWMA) chart for simultaneously monitoring the mean and/or variance of a normally distributed process. Unlike the MaxEWMA chart, the MaxAEWMA chart provides an overall good performance for detecting a range of the mean and dispersion shift sizes rather than a single value. The run length characteristics of the MaxAEWMA chart are computed using extensive Monte Carlo simulations. The MaxAEWMA chart is comprehensively compared with the MaxEWMA chart in terms of the average and standard deviation of the run length. It is found that the MaxAEWMA chart performs substantially and uniformly better than the MaxEWMA chart. An example is given to explain the implementation of the MaxEWMA and MaxAEWMA charts.