Control (management)

Process (computing)

Engineering

Statistics

Work in process

Operating system

Data mining

Computer science

Statistical process control

Mathematics

Nonparametric statistics

Econometrics

Control chart

Operations management

Artificial intelligence

Categorical variable

Machine learning

Process capability

Journal of Quality Technology

Statistical process control (SPC) charts play a central role in quality control and management. Many conventional SPC charts are designed under the assumption that the related process distribution is normal. In practice, the normality assumption is often invalid. In such cases, some articles show that certain conventional SPC charts are robust and can still be used as long as their parameters are properly chosen. Some other articles argue that results from such conventional SPC charts would not be reliable and that nonparametric SPC charts should be considered instead. In recent years, many nonparametric SPC charts have been proposed. Most of them are based on the ranking information in process observations collected at different time points. Some of them are based on data categorization and categorical data analysis. In this article, we give some perspectives on issues related to the robustness of conventional SPC charts and to the strengths and limitations of various nonparametric SPC charts.

Some perspectives on nonparametric statistical process control