{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T10:49:16Z","timestamp":1700563756276},"reference-count":25,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2009,8,7]],"date-time":"2009-08-07T00:00:00Z","timestamp":1249603200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Quality & Reliability Eng"],"published-print":{"date-parts":[[2010,7]]},"abstract":"Abstract<\/jats:title>The primary objective of multivariate statistical process control is to monitor the related process quality characteristics over time and identify the assignable causes affecting the process using multivariate control charts. When an out\u2010of\u2010control signal is obtained from the chart, it is imperative to be able to detect the component variables that have gone out\u2010of\u2010control. In this paper we propose a new charting procedure for T<\/jats:italic>2<\/jats:sup>, multivariate exponentially weighted moving average and multivariate cumulative sum control charts. The proposed charts will facilitate in identification of the source of out\u2010of\u2010control signal and are simple, economical and easier to implement. 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