An artificial neural network approach for improved demand estimation of a cool-disk manufacturer

Ali Azadeh, Behnam Beheshtipour and Mohsen Moghaddam

International Journal of Industrial and Systems Engineering, 2011, vol. 7, issue 3, 357-380

Abstract: Neural networks have successfully been used for demand forecasting. However, due to a large number of parameters to be estimated empirically, it is not a simple task to select the appropriate neural network architecture for a demand forecasting problem. So, in this paper, we examine the effects of the number of input and hidden nodes and hidden layers as well as the size of the training sample on the in-sample and out-of-sample performance. Also, we consider a new forecasting approach inspired from the regression method for weekly demand forecasting as a benchmark for comparison with ANN. It is shown that ANN is superior to a regression model through analysis of variance and well-known relative error estimation methods. This is the first study that presents an integrated ANN approach for improved demand estimation in a cool-disk manufacturer company.

Keywords: cool disks; demand forecasting; ANNs; artificial neural networks; multi-layer perception; ANOVA; analysis of variance; demand estimations; network architecture; input nodes; hidden nodes; hidden layers; out-of-sample performance; in-sample performance; training samples; regression methods; benchmarks; relative errors; computer hardware; industrial engineering; systems engineering. (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=38984 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:7:y:2011:i:3:p:357-380

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().