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Using principal component analysis in process performance for multivariate data

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  • Wang, F. K.
  • Du, T. C. T.

Abstract

Quality measures can be used to evaluate a process's performance. Analyzing related quality characteristics such as weight, width and height can be combined using multivariate statistical techniques. Recently, multivariate capability indices have been developed to assess the process capability of a product with multiple quality characteristics. This approach assumes multivariate normal distribution. However, obtaining these distributions can be a complicated task, making it difficult to derive the needed confidence intervals. Therefore, there is a need to develop one robust method to deal with the process performance on non-multivariate normal data. Principal component analysis (PCA) can transform the high-dimensional problems into lower dimensional problems and provide sufficient information. This method is particularly useful in analyzing large sets of correlated data. Also, the application of PCA does not require multivariate normal assumption. In this study, several capability indices are proposed to summarize the process performance using PCA. Also, the corresponding confidence intervals are derived. Real-world case studies will illustrate the value and power of this methodology.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jomega:v:28:y:2000:i:2:p:185-194
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    Citations

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    Cited by:

    1. J. N. Pan & Sheau-Chiann Chen, 2013. "Correlated Risk Assessment and Its Managerial Applications," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    2. Panagopoulos, Orestis P. & Pappu, Vijay & Xanthopoulos, Petros & Pardalos, Panos M., 2016. "Constrained subspace classifier for high dimensional datasets," Omega, Elsevier, vol. 59(PA), pages 40-46.
    3. Lei Wang & Yan Yan & Xiaoteng Li & Xiaosong Chen, 2018. "General Component Analysis (GCA): A new approach to identify Chinese corporate bond market structures," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-18, July.
    4. Noureddine Kouaissah & Sergio Ortobelli Lozza & Ikram Jebabli, 2022. "Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 833-859, October.
    5. 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.
    6. 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.
    7. Bo Xiong & Martin Skitmore & Bo Xia, 2015. "Exploring and validating the internal dimensions of occupational stress: evidence from construction cost estimators in China," Construction Management and Economics, Taylor & Francis Journals, vol. 33(5-6), pages 495-507, June.
    8. Daniela F. Dianda & Marta B. Quaglino & José A. Pagura, 2018. "Impact of measurement errors on the performance and distributional properties of the multivariate capability index $$\mathbf{NMC }_\mathbf{pm }$$ NMC pm," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 117-143, January.
    9. Pearn, W.L. & Wu, Chien-Wei, 2006. "Production quality and yield assurance for processes with multiple independent characteristics," European Journal of Operational Research, Elsevier, vol. 173(2), pages 637-647, September.
    10. Seebacher, Gottfried & Winkler, Herwig, 2015. "A capability approach to evaluate supply chain flexibility," International Journal of Production Economics, Elsevier, vol. 167(C), pages 177-186.
    11. Huda, Shamsul & Abdollahian, Mali & Mammadov, Musa & Yearwood, John & Ahmed, Shafiq & Sultan, Ibrahim, 2014. "A hybrid wrapper–filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process," European Journal of Operational Research, Elsevier, vol. 237(3), pages 857-870.
    12. Das Nandini & Dwivedi Prem Saurav, 2013. "Multivariate Process Capability Index: A Review and Some Results," Stochastics and Quality Control, De Gruyter, vol. 28(2), pages 151-166, December.

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