A unified generalised process capability index and its applications to logistic-exponential distributed characteristic

Mahendra Saha, Amartya Bhattacharya, Sukanta Pramanik, Sudhansu S. Maiti and Arindam Gupta

European Journal of Industrial Engineering, 2025, vol. 19, issue 4, 401-428

Abstract: The introduction of the process capability index has made it possible for industries to evaluate process performance and assess how well the final product meets consumer expectations. In this article, we take into consideration the six most popular estimation techniques, namely the maximum likelihood, least square, weighted least square, percentile, Cramèr-von-Mises, and maximum product of spacing techniques, in order to estimate the parameters and the new unified measure of the generalised process capability index, denoted as C_py (u, v) for the logistic-exponential process distribution. Extensive simulations are carried out to investigate the performances of these considered classical estimation methods in terms of their respective biases and mean squared errors. Additionally, we contrast the results of three bootstrap confidence intervals of C_py (u, v) in terms of average widths and coverage probabilities: standard bootstrap, percentile bootstrap, and bias-corrected percentile bootstrap. Two datasets related to the electronic industries are re-analysed in order to show the applicability of the suggested methodologies. [Submitted: 28 December 2022; Accepted: 17 October 2023]

Keywords: Monte-Carlo simulation; point and interval estimation; unified measure of process capability index. (search for similar items in EconPapers)
Date: 2025
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