IDEAS home Printed from https://ideas.repec.org/h/tkp/mklp17/605-612.html
   My bibliography  Save this book chapter

Dea Supported Ann Approach to Operational Efficiency Assessment of Smes

Author

Listed:
  • Hidayet Talha Kus

    (Istanbul University, Turkey)

  • Enis Bulak

    (Istanbul University, Turkey)

  • Ali Turkyilmaz

    (Nazarbayev University, Kazakhstan)

  • Zbigniew Pastuszak

    (Maria Curie-Sklodowska University, Poland)

Abstract

This study addresses to classify Turkish Small Medium Enterprises (SMEs) in terms of their efficiency scores by developing a DEA supported Neural Network classification model. For this purpose, 744 manufacturing companies from ten different industries are taken into consideration. First, by considering the input and output values of the firms, efficiency scores of the companies are calculated with Data Envelopment Analysis (DEA). Then, to perform the Artificial Neural Network (ANN) classification analysis, same inputs variables are used while the efficiency scores from the DEA model are used as target values. This DEA supported ANN model provides a powerful efficiency estimation of SMEs with 96.4% performance efficiency when their output measures (i.e. market share, profit margin etc.) are not available.

Suggested Citation

  • Hidayet Talha Kus & Enis Bulak & Ali Turkyilmaz & Zbigniew Pastuszak, 2017. "Dea Supported Ann Approach to Operational Efficiency Assessment of Smes," Management Challenges in a Network Economy: Proceedings of the MakeLearn and TIIM International Conference 2017,, ToKnowPress.
    • Handle: RePEc:tkp:mklp17:605-612
    as

    Download full text from publisher

    File URL: http://www.toknowpress.net/ISBN/978-961-6914-21-5/papers/ML17-144.pdf
    File Function: full text
    Download Restriction: no
    File URL: http://www.toknowpress.net/ISBN/978-961-6914-21-5/MakeLearn2017.pdf
    File Function: Conference Programme
    Download Restriction: no
    ---><---

    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:tkp:mklp17:605-612. 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: Miha Jezovnik (email available below). General contact details of provider: http://www.toknowpress.net/proceedings/978-961-6914-21-5/ .

    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.