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

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  • L.S. Dharmasena
  • P. Zeephongsekul

Abstract

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 which can take on any real number. This MPCI generalises some existing multivariate indices based on PCA proposed by several authors when or . 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 . 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.

Suggested Citation

  • L.S. Dharmasena & P. Zeephongsekul, 2016. "A new process capability index for multiple quality characteristics based on principal components," International Journal of Production Research, Taylor & Francis Journals, vol. 54(15), pages 4617-4633, August.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:15:p:4617-4633
    DOI: 10.1080/00207543.2015.1091520
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    References listed on IDEAS

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    1. Wu, Chien-Wei & Pearn, W.L. & Kotz, Samuel, 2009. "An overview of theory and practice on process capability indices for quality assurance," International Journal of Production Economics, Elsevier, vol. 117(2), pages 338-359, February.
    2. Wang, F. K. & Du, T. C. T., 2000. "Using principal component analysis in process performance for multivariate data," Omega, Elsevier, vol. 28(2), pages 185-194, April.
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    Cited by:

    1. Kuen-Suan Chen & Chun-Min Yu, 2022. "Lifetime performance evaluation and analysis model of passive component capacitor products," Annals of Operations Research, Springer, vol. 311(1), pages 51-64, April.

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