Sample size determination for estimating multivariate process capability indices based on lower confidence limits

Abstract

With the advent of modern technology, manufacturing processes have become very sophisticated; a single quality characteristic can no longer reflect a product's quality. In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, several new multivariate capability (NMC) indices, such as NMC _p and NMC _pm , have been developed over the past few years. However, the sample size determination for multivariate process capability indices has not been thoroughly considered in previous studies. Generally, the larger the sample size, the more accurate an estimation will be. However, too large a sample size may result in excessive costs. Hence, the trade-off between sample size and precision in estimation is a critical issue. In this paper, the lower confidence limits of NMC _p and NMC _pm indices are used to determine the appropriate sample size. Moreover, a procedure for conducting the multivariate process capability study is provided. Finally, two numerical examples are given to demonstrate that the proper determination of sample size for multivariate process indices can achieve a good balance between sampling costs and estimation precision.

Keywords:

• lower confidence limit
• multivariate capability indices
• sample size determination
• statistical quality control
• estimation precision

Acknowledgements

We sincerely thank the two anonymous reviewers for their helpful comments. This work was supported by NIH/NCI research grants P30 CA068485, P50 CA090949, P50 CA095103, and P50 CA098131.