Parametric inference of the process capability index for exponentiated exponential distribution

Abstract

Process capability indices (PCIs) are most effective devices/techniques used in industries for determining the quality of products and performance of manufacturing processes. In this article, we consider the PCI C_pc which is based on the proportion of conformance and is applicable to normally as well as non-normally and continuous as well as discrete distributed processes. In order to estimate the PCI C_pc when the process follows exponentiated exponential distribution, we have used five classical methods of estimation. The performances of these classical estimators are compared with respect to their biases and mean squared errors (MSEs) of the index C_pc through simulation study. Also, the confidence intervals for the index C_pc are constructed using five bootstrap confidence interval (BCIs) methods. Monte Carlo simulation study has been carried out to compare the performances of these five BCIs in terms of their average width and coverage probabilities. Besides, net sensitivity (NS) analysis for the given PCI C_pc is considered. We use two data sets related to electronic and food industries and two failure time data sets to illustrate the performance of the proposed methods of estimation and BCIs. Additionally, we have developed PCI C_pc using aforementioned methods for generalized Rayleigh distribution.

Keywords:

• Bootstrap confidence interval
• classical methods of estimation
• exponentiated exponential distribution
• generalized Rayleigh distribution
• Monte Carlo simulation
• process capability index

Acknowledgments

Authors thank the Editor and Associate Editor of the Journal, and three anonymous reviewers for their constructive suggestions which helped us to improve the earlier version of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).