About The Run Length Properties for ( Cumulative Sum(Cusum) and The Exponentially Weighted Moving Average (EWMA)) control charts for Poisson Distribution

Authors

  • جنان عباس ناصر

DOI:

https://doi.org/10.33095/jeas.v25i114.1747

Keywords:

/ لوحات السيطرة، المجموع المتراكم، لوحة المتوسط المتحرك الموزون اسيا، سلاسل ماركوف، توزيع بواسون، متوسط طول التشغيل (ARL)، الانحراف المعياري لطول التشغيل (SDRL)., control charts, cumulative sum (Cusum), the Exponentially Weighted Moving Average (EWMA) , markov chains, the poisson distribution, Average Run Length (ARL), Standard Deviation Run Length (SDRL).

Abstract

     In this study, we investigate about the run length properties of cumulative sum (Cusum) and The exponentially weighted moving average (EWMA) control charts, to detect positive shifts in the mean of the process for the poisson distribution with unknown mean. We used markov chain approach to compute the average and the standard deviation for run length for Cusum and EWMA control charts, when the variable under control follows poisson distribution. Also, we used the Cusum and the EWMA control charts for monitoring a process mean when the observations (products are selected from Al_Mamun Factory ) are identically and independently distributed (iid) from poisson distribution in continuous manufacturing .We assumed several values for the parameters of the poisson Cusum and the poisson EWMA control charts, and several state numbers for markov chain. The results were obtained by using Programs written using matlab-R2018a program .The results shown that poisson Cusum and poisson EWMA control charts control charts for poisson distribution were more sensitive at certain values for the parameters of the Cusum and the EWMA control charts. at certain values for the state number of markov chain.

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Published

2019-10-01

Issue

Section

Statistical Researches

How to Cite

ناصر ج.ع. (2019) “About The Run Length Properties for ( Cumulative Sum(Cusum) and The Exponentially Weighted Moving Average (EWMA)) control charts for Poisson Distribution”, Journal of Economics and Administrative Sciences, 25(114), pp. 475–497. doi:10.33095/jeas.v25i114.1747.

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