IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v49y2015i1p115-133.html
   My bibliography  Save this article

Object-oriented Bayesian networks for complex quality management problems

Author

Listed:
  • Flaminia Musella

    ()

  • Paola Vicard

    ()

Abstract

Quality management and customer satisfaction evaluation can be difficult tasks to perform when processes involve multiple production lines or provide multichannel services. As a consequence, the top management needs to analyse the problem from different perspectives, to evaluate possible improvement strategies at several levels and to take appropriate decisions. To this aim, we propose to use object-oriented Bayesian networks by which different quality aspects and evaluations can be integrated in a unique framework allowing to analyse improvement strategies in real time. We show, by an application to an internal-customer satisfaction survey, how to combine the perceived quality of different production areas and how to evaluate the impact on the global quality of improvement actions developed in one or more areas. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Flaminia Musella & Paola Vicard, 2015. "Object-oriented Bayesian networks for complex quality management problems," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 115-133, January.
  • Handle: RePEc:spr:qualqt:v:49:y:2015:i:1:p:115-133
    DOI: 10.1007/s11135-013-9977-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-013-9977-3
    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.

    References listed on IDEAS

    as
    1. Julia Mortera & Paola Vicard & Cecilia Vergari, 2012. "Object-Oriented Bayesian Networks for a Decision Support System," Departmental Working Papers of Economics - University 'Roma Tre' 0144, Department of Economics - University Roma Tre.
    2. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
    3. Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
    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. repec:eee:transa:v:106:y:2017:i:c:p:235-247 is not listed on IDEAS

    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:spr:qualqt:v:49:y:2015:i:1:p:115-133. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.