Univariate X̄ control charts for individual characteristics in a multinormal model
Author
Serel, D. A.
Moskowitz, H.
Tang, J.
Date
2000Source Title
IIE Transactions
Print ISSN
0740-817X
Electronic ISSN
1545-8830
Publisher
Taylor & Francis
Volume
32
Issue
12
Pages
1115 - 1125
Language
English
Type
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Show full item recordAbstract
The early work on multivariate statistical process control was built upon Hotelling's T2 control chart which was developed to simultaneously monitor the means of correlated quality variables. This chart, however, has a drawback, namely, the problem of identifying the responsible variable(s) when an out-of-control signal occurs. One alternative is to use a separate X̄ control chart for each individual characteristic with equal risks, based on Bonferroni inequality. In this study, we show that, from an economic perspective, it may be desirable to have unequal type I risks for the individual charts, because of different inspection and restoration costs associated with each variable. We obtain their risk ratios, which are measures of relative importance of the variables monitored. Then, based on these risk ratios, we develop computer algorithms for finding the exact control limits for individual variables from a multinormal distribution, in the sense that the overall type I risk of the charts is equal to the desired value. Numerical studies show that the proposed methods give optimal or near-optimal results from an economic as well as statistical point of view.
Keywords
AlgorithmsCost effectiveness
Inspection
Mathematical models
Probability distributions
Quality control
Statistical methods
Bonferroni inequality
Control charts
Multinormal distribution
Univariate Shewhart charts
Statistical process control