Machine Learning-Based Demand Forecasting in Supply Chains

Real Carbonneau, Rustam Vahidov and Kevin Laframboise
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Real Carbonneau: Concordia University, Canada
Rustam Vahidov: Concordia University, Canada
Kevin Laframboise: Concordia University, Canada

International Journal of Intelligent Information Technologies (IJIIT), 2007, vol. 3, issue 4, 40-57

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.

Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:3:y:2007:i:4:p:40-57

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