IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v54y2016i15p4617-4633.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2015.1091520
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2015.1091520?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Wu, Chien-Wei, 2012. "An efficient inspection scheme for variables based on Taguchi capability index," European Journal of Operational Research, Elsevier, vol. 223(1), pages 116-122.
    3. Chien-Wei Wu & Zih-Huei Wang, 2017. "Developing a variables multiple dependent state sampling plan with simultaneous consideration of process yield and quality loss," International Journal of Production Research, Taylor & Francis Journals, vol. 55(8), pages 2351-2364, April.
    4. Chien-Wei Wu & Ming-Hung Shu & Pei-An Wang & Bi-Min Hsu, 2021. "Variables skip-lot sampling plans on the basis of process capability index for products with a low fraction of defectives," Computational Statistics, Springer, vol. 36(2), pages 1391-1413, June.
    5. Peruchi, Rogério Santana & Balestrassi, Pedro Paulo & de Paiva, Anderson Paulo & Ferreira, João Roberto & de Santana Carmelossi, Michele, 2013. "A new multivariate gage R&R method for correlated characteristics," International Journal of Production Economics, Elsevier, vol. 144(1), pages 301-315.
    6. Wu, Chien-Wei & Aslam, Muhammad & Jun, Chi-Hyuck, 2012. "Variables sampling inspection scheme for resubmitted lots based on the process capability index Cpk," European Journal of Operational Research, Elsevier, vol. 217(3), pages 560-566.
    7. 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.
    8. Li, Der-Chiang & Lin, Liang-Sian, 2013. "A new approach to assess product lifetime performance for small data sets," European Journal of Operational Research, Elsevier, vol. 230(2), pages 290-298.
    9. Michele Scagliarini, 2011. "Multivariate process capability using principal component analysis in the presence of measurement errors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 113-128, June.
    10. 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.
    11. 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.
    12. Marta Benková & Dagmar Bednárová & Gabriela Bogdanovská, 2024. "Process Capability Evaluation Using Capability Indices as a Part of Statistical Process Control," Mathematics, MDPI, vol. 12(11), pages 1-29, May.
    13. 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.
    14. Pedro Veiga & Luis Mendes & Luis Lourenço, 2016. "A retrospective view of statistical quality control research and identification of emerging trends: a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 673-692, March.
    15. George Sebastian & Sasi Ajitha, 2017. "Bootstrap Lower Confidence Limits of Superstructure Process Capability Indices for Esscher-Transformed Laplace Distribution," Stochastics and Quality Control, De Gruyter, vol. 32(2), pages 87-98, December.
    16. 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.
    17. 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.
    18. Muhammad Aslam & Mohammed Albassam, 2019. "Inspection Plan Based on the Process Capability Index Using the Neutrosophic Statistical Method," Mathematics, MDPI, vol. 7(7), pages 1-10, July.
    19. 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.
    20. Kuen-Suan Chen & Tsang-Chuan Chang & Chien-Che Huang, 2020. "Supplier Selection by Fuzzy Assessment and Testing for Process Quality under Consideration with Data Imprecision," Mathematics, MDPI, vol. 8(9), pages 1-14, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tprsxx:v:54:y:2016:i:15:p:4617-4633. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.