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Economic design of the integrated multivariate EPC and multivariate SPC charts
Author(s) -
Yang Ling,
Sheu SheyHuei
Publication year - 2007
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.785
Subject(s) - multivariate statistics , control chart , statistical process control , euclidean distance , chart , multivariate analysis , statistics , shewhart individuals control chart , computer science , multivariate normal distribution , ewma chart , process (computing) , reliability engineering , data mining , engineering , mathematics , artificial intelligence , operating system
The goal of engineering process control (EPC) is to minimize variability by adjusting some manipulative process variables. The goal of statistical process control (SPC) is to reduce variability by monitoring and eliminating assignable causes of variation. As suggested by Box and Kramer and others, it is possible to reduce both special cause and common cause variations by integrating EPC and SPC. In the integrated multivariate EPC (MEPC) and multivariate SPC (MSPC) charts, we propose some statistical and economic criteria, such as the average Euclidean distance from the target vector and the average quality cost ( AQC ) to evaluate the performance of the MEPC/MSPC charts. The traditional average run length ( ARL ), average Euclidean distance and AQC of three MSPC charts are investigated and compared. The results of the simulations show that the MEPC/MGWMA chart is more effective and more economical than both the MEPC/MEWMA chart and the MEPC/Hotelling multivariate chart in detecting small shifts of the mean vector. Copyright © 2006 John Wiley & Sons, Ltd.