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

## TL;DR

This paper introduces a set of simple, powerful nonparametric methods for statistical process control using sequential normal scores, applicable to univariate and multivariate data, with demonstrated effectiveness through numerous examples and tests.

## Contribution

It presents new nonparametric control methods based on sequential normal scores, simplifying existing techniques while maintaining high detection power.

## Key findings

- Methods are simple to implement and interpret.
- Effective in detecting location and scale changes.
- Validated through extensive examples and statistical tests.

## Abstract

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.