IDEAS home Printed from https://ideas.repec.org/a/ids/ijisen/v15y2013i1p1-18.html
   My bibliography  Save this article

A neural approach to product demand forecasting

Author

Listed:
  • Nafisa Mahbub
  • Sanjoy Kumar Paul
  • Abdullahil Azeem

Abstract

This paper develops an artificial neural network (ANN) model to forecast the optimum demand as a function of time of the year, festival period, promotional programmes, holidays, number of advertisements, cost of advertisements, number of workers and availability. The model selects a feed-forward back-propagation ANN with 13 hidden neurons in one hidden layer as the optimum network. The model is validated with a furniture product data of a renowned furniture company. The model has also been compared with a statistical linear model named Brown's double smoothing model which is normally used by furniture companies. It is observed that ANN model performs much better than the linear model. Overall, the proposed model can be applied for forecasting optimum demand level of furniture products in any furniture company within a competitive business environment.

Suggested Citation

  • Nafisa Mahbub & Sanjoy Kumar Paul & Abdullahil Azeem, 2013. "A neural approach to product demand forecasting," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 15(1), pages 1-18.
    • Handle: RePEc:ids:ijisen:v:15:y:2013:i:1:p:1-18
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=55508
    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:ijisen:v:15:y:2013:i:1:p:1-18. 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=188 .

    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.