Machine Learning-Based Demand Forecasting in Supply Chains

Machine Learning-Based Demand Forecasting in Supply Chains

Real Carbonneau (Concordia University, Canada), Rustam Vahidov (Concordia University, Canada) and Kevin Laframboise (Concordia University, Canada)
Copyright: © 2007 |Pages: 18
DOI: 10.4018/jiit.2007100103

Abstract

Effective supply chain management is one of the key determinants of success of today’s businesses. However, communication patterns between participants that emerge in a supply chain tend to distort the original consumer’s demand and create high levels of noise. In this article, we compare the performance of new machine learning (ML)-based forecasting techniques with the more traditional methods. To this end we used the data from a chocolate manufacturer, a toner cartridge manufacturer, as well as from the Statistics Canada manufacturing survey. A representative set of traditional and ML-based forecasting techniques have been applied to the demand data and the accuracy of the methods was compared. As a group, based on ranking, the average performance of the ML techniques does not outperform the traditional approaches. However, using a support vector machine (SVM) that is trained on multiple demand series has produced the most accurate forecasts.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 15: 4 Issues (2019): Forthcoming, Available for Pre-Order
Volume 14: 4 Issues (2018): 1 Released, 3 Forthcoming
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing