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To maintain the desired quality of a product or service it is necessary tomonitor the process that results in the product or service. This monitoringmethod is called Statistical Process Management, or Statistical ProcessControl. It is in widespread usage in industry. Extensive statisticalmethodology has been developed to make it possible to detect when a processgoes out of control while allowing for natural variability that occurs when theprocess is in control. This paper introduces nonparametric methods formonitoring data, whether it is univariate or multivariate, and whether theinterest is in detecting a change of location or scale or both. These methods,based on sequential normal scores, are much simpler than the most popularnonparametric methods currently in use and have good power for detecting out ofcontrol observations. Sixteen new statistical tests are introduced for thefirst time in this paper, with 17 examples, 33 tables, and 48 figures tocomplete the instructions for their application.

An Approach to Statistical Process Control that is New, Nonparametric, Simple, and Powerful