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A proposed variable parameter control chart for monitoring the multivariate coefficient of variation
Author(s) -
Chew XinYing,
Khoo Michael Boon Chong,
Khaw Khai Wah,
Yeong Wai Chung,
Chong Zhi Lin
Publication year - 2019
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2536
Subject(s) - control chart , chart , x bar chart , standard deviation , ewma chart , statistics , shewhart individuals control chart , coefficient of variation , markov chain , control limits , multivariate statistics , variance (accounting) , computer science , statistical process control , mathematics , process (computing) , operating system , accounting , business
An efficient process monitoring system is important for achieving sustainable manufacturing. The control charting technique is one of the most effective techniques to monitor process quality. In certain processes where the process mean and variance are not independent of one another, the coefficient of variation (CV), which measures the ratio of the standard deviation to the mean, should be monitored. In line with industrial settings, where at least two or more variables are monitored simultaneously in most processes, this paper proposes a variable parameter (VP) chart to monitor the multivariate CV (MCV). Formulae and algorithms to optimize the various performance measures are discussed. The proposed VP MCV chart is designed based on a Markov chain approach. The performance comparison shows that the proposed VP MCV chart prevails over the existing MCV charts, in terms of the average time to signal (ATS), standard deviation of the time of signal (SDTS), and expected average time to signal (EATS) criteria. An example is presented to illustrate the implementation of the proposed VP MCV chart.