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Computing process capability indices for non‐normal data: a review and comparative study
Author(s) -
Tang Loon Ching,
Than Su Ee
Publication year - 1999
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/(sici)1099-1638(199909/10)15:5<339::aid-qre259>3.0.co;2-a
Subject(s) - normality , kurtosis , process capability , skewness , weibull distribution , log normal distribution , normal distribution , computer science , process (computing) , data mining , process capability index , statistics , econometrics , reliability engineering , mathematics , engineering , work in process , operations management , operating system
When the distribution of a process characteristic is non‐normal, C p and C pk calculated using conventional methods often lead to erroneous interpretation of the process's capability. Though various methods have been proposed for computing surrogate process capability indices (PCIs) under non‐normality, there is a lack of literature that covers a comprehensive evaluation and comparison of these methods. In particular, under mild and severe departures from normality, do these surrogate PCIs adequately capture process capability, and which is the best method(s) in reflecting the true capability under each of these circumstances? In this paper we review seven methods that are chosen for performance comparison in their ability to handle non‐normality in PCIs. For illustration purposes the comparison is done through simulating Weibull and lognormal data, and the results are presented using box plots. Simulation results show that the performance of a method is dependent on its capability to capture the tail behaviour of the underlying distributions. Finally we give a practitioner's guide that suggests applicable methods for each defined range of skewness and kurtosis under mild and severe departures from normality. Copyright © 1999 John Wiley & Sons, Ltd.