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