Title: An artificial neural network approach for improved demand estimation of a cool-disk manufacturer
Author: Ali Azadeh, Behnam Beheshtipour, Mohsen Moghaddam
Department of Industrial Engineering, Centre of Excellence for Intelligent Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Centre of Excellence for Intelligent Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran
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.
Int. J. of Industrial and Systems Engineering, 2011 Vol.7, No.3, pp.357 - 380
Available online: 10 Mar 2011