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
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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.

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