Monitoring the coefficient of variation using a variable sample size EWMA chart

2018 ◽  
Vol 126 ◽  
pp. 378-398 ◽  
Author(s):  
Anis Nabila Binti Muhammad ◽  
Wai Chung Yeong ◽  
Zhi Lin Chong ◽  
Sok Li Lim ◽  
Michael Boon Chong Khoo
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Variable Sample Size ◽  
Ewma Chart

A variable sample size run sum coefficient of variation chart

2022 ◽  
Author(s):  
Wai Chung Yeong ◽  
Yen Yoon Tan ◽  
Sok Li Lim ◽  
Khai Wah Khaw ◽  
Michael Boon Chong Khoo
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Variable Sample Size

Monitoring the coefficient of variation using a variable sample size control chart

2015 ◽  
Vol 80 (9-12) ◽  
pp. 1561-1576 ◽  
Author(s):  
Philippe Castagliola ◽  
Ali Achouri ◽  
Hassen Taleb ◽  
Giovanni Celano ◽  
Stelios Psarakis
Keyword(s):  
Sample Size ◽  
Control Chart ◽  
Coefficient Of Variation ◽  
Size Control ◽  
Variable Sample Size

scholarly journals Variable Sample Size EWMA Chart With Measurement Errors

2019 ◽  
Vol 0 (0) ◽  
pp. 0-0 ◽  
Author(s):  
Zeynab Hassani ◽  
Amirhossein Amiri ◽  
Philippe CASTAGLIOLA
Keyword(s):  
Sample Size ◽  
Measurement Errors ◽  
Variable Sample Size ◽  
Ewma Chart

A VARIABLE SAMPLE SIZE S2-EWMA CONTROL CHART FOR MONITORING THE PROCESS VARIANCE

2008 ◽  
Vol 15 (03) ◽  
pp. 181-201 ◽  
Author(s):  
PHILIPPE CASTAGLIOLA ◽  
GIOVANNI CELANO ◽  
SERGIO FICHERA ◽  
VALERIA NUNNARI
Keyword(s):  
Sample Size ◽  
Control Chart ◽  
Quality Parameter ◽  
Operating Conditions ◽  
Design Parameters ◽  
Statistical Process ◽  
Variable Sample Size ◽  
Ewma Chart ◽  
Ewma Control Chart ◽  
Process Dispersion

Monitoring the stability of measures dispersion from a process quality parameter is an important aspect of Statistical Process Control which should be carefully planned by practitioners. To perform this task, this paper proposes an adaptive EWMA chart as a practical and efficient tool. The developed EWMA chart is the Variable Sample Size (VSS) version of a static S2-EWMA control chart previously developed by one of the authors to monitor the sample variance. The way to compute the design parameters of this VSS S2-EWMA control chart is discussed and an optimal design strategy based on the Average Time to Signal (ATS) after a shift in process dispersion is presented. The statistical performance of the VSS S2-EWMA has been evaluated by means of a comparison with two other EWMA charts: the static S2-EWMA and the adaptive (VSI) S2-EWMA allowing to vary the sampling intervals. The obtained results show how the possibility of varying the sample size significantly improves the statistical performance over the static S2-EWMA; furthermore, some interesting findings suggest to implement the VSS S2-EWMA with respect to the VSI S2-EWMA when some particular process operating conditions occur.

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