Permutation Methods for Comparing Process Capabilities

Abstract

The statistical comparison of competing manufacturing processes is an important aspect of statistical quality control that aids quality managers in the choice of potential suppliers of products, manufacturing methodologies, or proposed adjustments to the manufacturing process. The selection criteria, in the absence of considerations such as cost, is based on a quality metric, such as the capability of each manufacturing process. Because quality metrics are estimated based on sample process data, the inherent variability in the estimates must be accounted for when selecting the best manufacturing process. In this paper, we consider solutions to this problem based on permutation testing methodology. In the case of two processes, the methodology is based on a simple permutation test of the null hypothesis that the two processes have equal capability. In the case of more than two processes, multiple-comparison techniques are used in conjunction with the proposed permutation tests. The advantage of using the permutation methods is that the significance levels of the permutation tests are exact regardless of the distribution of the process data. The methodology is demonstrated using several examples, and the potential performance of the methods are investigated empirically.

• Bonferroni
• Bootstrap
• Multiple Comparisons
• p-Value
• Randomization

Additional information

Notes on contributors

Alan M. Polansky

Dr. Polansky is an Associate Professor of Statistics in the Division of Statistics. He is a member of ASQ. His email address is [email protected].