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Process Capability and Performance Indices for Discrete Data

Author

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  • Vasileios Alevizakos

    (Department of Mathematics, National Technical University of Athens, Zografou, 15773 Athens, Greece)

Abstract

Process capability and performance indices (PCIs and PPIs) are used in industry to provide numerical measures for the capability and performance of several processes. The majority of the literature refers to PCIs and PPIs for continuous data. The aim of this paper is to compute the classical indices for discrete data following Poisson, binomial or negative binomial distribution using various transformation techniques. A simulation study under different situations of a process and comparisons with other existing PCIs for discrete data are also presented. The methodology of computing the indices is easy to use, and as a result, one can have an assessment of the process capability and performance without difficulty. Three examples are further provided to illustrate the application of the transformation techniques.

Suggested Citation

  • Vasileios Alevizakos, 2023. "Process Capability and Performance Indices for Discrete Data," Mathematics, MDPI, vol. 11(16), pages 1-22, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3457-:d:1213878
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    References listed on IDEAS

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    1. Sanku Dey & Mahendra Saha, 2019. "Bootstrap confidence intervals of generalized process capability index Cpyk using different methods of estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(10), pages 1843-1869, July.
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    Cited by:

    1. Aylin Pakzad & Ali Yeganeh & Rassoul Noorossana & Sandile Charles Shongwe, 2024. "Process Capability Index for Simple Linear Profile in the Presence of Within- and Between-Profile Autocorrelation," Mathematics, MDPI, vol. 12(16), pages 1-40, August.

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