Statistical design of X charts for autocorrelated processes for different sample sizes Online publication date: Fri, 31-Oct-2014
by D.R. Prajapati; Sukhraj Singh
International Journal of Productivity and Quality Management (IJPQM), Vol. 14, No. 4, 2014
Abstract: The X charts for variables are widely used in industries to detect the shift in the process mean. As in actual practice, some process outputs are correlated and conventional charts may not be effective in such situation. The performance of the chart is measured in terms of the average run length (ARL) that is the average number of samples before getting an out-of-control signal. Ultimately the performance of the chart is suspected due to the effect of correlation. The ARLs at various sets of parameters of the X chart are computed by simulation, using MATLAB. An attempt has been made to counter autocorrelation by designing the X chart, using warning limits. Various optimal schemes of modified X chart are proposed for various sample sizes (n) at the levels of correlation (Φ) of 0.00, 0.475 and 0.95. These optimal schemes of modified X chart are compared with the double sampling (DS) X chart, suggested by Costa and Claro (2008). It is concluded that the modified X chart outperforms the DS chart at various levels of correlation (Φ) and shifts in the process mean. The simplicity in the design of modified X chart makes it versatile for many industries.
Online publication date: Fri, 31-Oct-2014
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Productivity and Quality Management (IJPQM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com