Impact of measurement error on maximum hybrid exponentially weighted moving average control chart

Document Type : Review Article

Authors

1 COMSATS University Islamabad, Lahore Campus, Pakistan

2 Pakistan Bureau of Statistics, Islamabad, Pakistan

3 National College of Business Administration and Economics, Lahore, Pakistan

4 Qatar University, Doha, Qatar

Abstract

Statistical process control provides various types of control charts for monitoring of mean and variance shifts in the industrial production process individually as well as jointly to improve and maintain the quality products. The authors proposed these control charts based on sample values selected to calculate the desired statistics assuming that these values are measured correctly. But in a real life situation, measurements of the values may suffer from errors ultimately affecting the efficiency of the control charts. A few of the researchers in the field of control charts also discussed the problem of measurement error during process monitoring and proposed solutions to avoid losses of producers. We also present a hybrid exponentially weighted moving average control chart for joint monitoring of mean as well as variance and study the effect of measurement error on the efficiency of this control chart and name it as Max-HEWMAME control chart. The impact of measurement error has been shown in the calculations and presented in the shape of average run lengths ( ) and standard deviations of run lengths ( ) using the Monte Carlo simulation method. A real life example is also included to support the simulation results.

Keywords

References

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Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 15 November 2021

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