Open AccessAn Optimal Adaptive Variable Sample Size Scheme for the Multivariate Coefficient of Variation
Author(s) -
Khai Wah Khaw,
XinYing Chew,
Ming Ha Lee,
Wai Chung Yeong
Publication year - 2021
Publication title -
statistics, optimization and information computing
Language(s) - English
Resource type - Journals
eISSN - 2311-004X
pISSN - 2310-5070
DOI - 10.19139/soic-2310-5070-996
Subject(s) - chart , x bar chart , standard deviation , statistics , ewma chart , coefficient of variation , multivariate statistics , variance (accounting) , sample size determination , mathematics , markov chain , control chart , process (computing) , computer science , accounting , business , operating system
Development of an efficient process monitoring system has always received great attention. Previous studies revealed that the coefficient of variation (CV) is important in ensuring process quality, especially for monitoring a process where its process mean and variance are highly correlated. The fact that almost all industrial process monitoring involves a minimum of two or more related quality characteristics being monitored simultaneously, this paper incorporates the salient feature of the adaptive sample size VSS scheme into the standard multivariate CV (MCV) chart, called the VSS MCV chart. A Markov chain model is developed for the derivation of the chart’s performance measures, i.e the average run length (ARL), the standard deviation of the run length (SDRL), the average sample size (ASS), the average number of observations to signal (ANOS) and the expected average run length (EARL). The numerical comparison shows that the proposed chart prevails over the existing standard MCV chart for detecting small and moderate upward and downward MCV shifts.