A new process capability index for multiple quality characteristics based on principal components

Dharmasena, L. S. and Zeephongsekul, P. 2016, A new process capability index for multiple quality characteristics based on principal components, International journal of production research, vol. 54, no. 15, pp. 4617-4633, doi: 10.1080/00207543.2015.1091520.

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Title A new process capability index for multiple quality characteristics based on principal components
Author(s) Dharmasena, L. S.ORCID iD for Dharmasena, L. S. orcid.org/0000-0002-3362-4819
Zeephongsekul, P.
Journal name International journal of production research
Volume number 54
Issue number 15
Start page 4617
End page 4633
Total pages 17
Publisher Taylor & Francis
Place of publication London, Eng.
Publication date 2016
ISSN 1366-588X
Keyword(s) multivariate process capability index
process yield
hypothesis tests
confidence intervals
Summary This paper presents a new multivariate process capability index (MPCI) which is based on the principal component analysis (PCA) and is dependent on a parameter (Formula presented.) which can take on any real number. This MPCI generalises some existing multivariate indices based on PCA proposed by several authors when (Formula presented.) or (Formula presented.). One of the key contributions of this paper is to show that there is a direct correspondence between this MPCI and process yield for a unique value of (Formula presented.). This result is used to establish a relationship between the capability status of the process and to show that under some mild conditions, the estimators of this MPCI is consistent and converge to a normal distribution. This is then applied to perform tests of statistical hypotheses and in determining sample sizes. Several numerical examples are presented with the objective of illustrating the procedures and demonstrating how they can be applied to determine the viability and capacity of different manufacturing processes.
Language eng
DOI 10.1080/00207543.2015.1091520
Field of Research 010401 Applied Statistics
010405 Statistical Theory
Socio Economic Objective 861403 Industrial Machinery and Equipment
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Taylor & Francis
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079049

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