IDEAS home Printed from https://ideas.repec.org/a/bpj/ecqcon/v38y2023i2p95-107n1.html
   My bibliography  Save this article

Measuring One-Sided Process Capability Index for Autocorrelated Data in the Presence of Random Measurement Errors

Author

Listed:
  • Bera Kuntal

    (SQC & OR Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata, - 700108, India)

  • Anis M. Z.

    (SQC & OR Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata, - 700108, India)

Abstract

Many quality characteristics in manufacturing industry are of one sided specifications. The well-known process capability indices CP⁢UC_{PU} and CP⁢LC_{PL} are often used to measure the performance of such type of production process. It is usually assumed that process observations are independent and measurement system is free of errors. But actually in many industry it has been proven that auto-correlation is an inherent nature of the production process, especially for chemical processes. Moreover, even with the use of highly sophisticated advanced measuring instruments some amount of measurement error is always present in the observed data. Hence gauge measurement error also needs to be considered. In this paper we discuss some inferential properties of one-sided process capability indices for a stationary Gaussian process in the presence of measurement errors. As a particular case of a stationary Gaussian process, we discuss the case of a stationary AR⁡(1)\operatorname{AR}(1) process where measurement error follows an independent Gaussian distribution.

Suggested Citation

  • Bera Kuntal & Anis M. Z., 2023. "Measuring One-Sided Process Capability Index for Autocorrelated Data in the Presence of Random Measurement Errors," Stochastics and Quality Control, De Gruyter, vol. 38(2), pages 95-107, December.
    • Handle: RePEc:bpj:ecqcon:v:38:y:2023:i:2:p:95-107:n:1
    DOI: 10.1515/eqc-2023-0020
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/eqc-2023-0020
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/eqc-2023-0020?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.

    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:bpj:ecqcon:v:38:y:2023:i:2:p:95-107:n:1. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.