IDEAS home Printed from https://ideas.repec.org/a/ids/ijpqma/v25y2018i3p305-330.html
   My bibliography  Save this article

A probabilistic model for predicting service level adherence of application support projects

Author

Listed:
  • Srijith Sreenivasan
  • Manimaran Sundaram

Abstract

Application support projects are those which maintain the operations of software in production environment. Service level adherence is a key outcome metric which monitors the contractual compliance of these projects with customers. This paper discusses an approach to develop a prediction model using process factors impacting service level adherence. These factors are derived from the processes and sub-processes having a logical relationship with the outcome. A total of 23 factors were considered initially for modelling, of which six were found to be statistically significant. The model, developed using regression and Monte Carlo simulation, is validated in an actual industrial project to ensure its accuracy. The model can be used in application support projects to arrive at timely decisions for suitable mid-course corrections to control service level adherence.

Suggested Citation

  • Srijith Sreenivasan & Manimaran Sundaram, 2018. "A probabilistic model for predicting service level adherence of application support projects," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 25(3), pages 305-330.
    • Handle: RePEc:ids:ijpqma:v:25:y:2018:i:3:p:305-330
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=95648
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijpqma:v:25:y:2018:i:3:p:305-330. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=177 .

    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.