Effect of autocorrelation on the performance of EWMA chart

Sukhraj Singh and D.R. Prajapati

International Journal of Productivity and Quality Management, 2012, vol. 9, issue 2, 177-193

Abstract: The outputs from the manufacturing processes are often assumed to be independent but actually the observations are correlated and when this correlation builds-up automatically in the entire process, it is called autocorrelation. The performance of a chart is measured in terms of average run length (ARL). The performance of the traditional exponentially-weighted moving average (EWMA) chart is studied under the effect of the positive correlation. ARL at various levels of correlation (Φ), weightage factor (λ) and at various width of control limits (K), are studied using simulation with MATLAB software. Optimal schemes of EWMA chart are proposed for each level of correlation and showed better performance compared to EWMA chart, suggested by Zhang (2000). Moreover, optimal schemes of EWMA chart at given weighting factor, λ = 0.2, are very much comparable with the EWMA stationary chart, proposed by Winkel and Zhang (2004) at the various levels of the correlation.

Keywords: EWMA control charts; exponentially-weighted moving average; ARL; average run length; coefficient of correlation; weightage factor; width of control limits; MATLAB; SPC; statistical process control; autocorrelation. (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=45191 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpqma:v:9:y:2012:i:2:p:177-193

Access Statistics for this article

More articles in International Journal of Productivity and Quality Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().