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Machine Learning-Based Demand Forecasting in Supply Chains

Author

Listed:
  • Real Carbonneau

    (Concordia University, Canada)

  • Rustam Vahidov

    (Concordia University, Canada)

  • Kevin Laframboise

    (Concordia University, Canada)

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.

Suggested Citation

  • Real Carbonneau & Rustam Vahidov & Kevin Laframboise, 2007. "Machine Learning-Based Demand Forecasting in Supply Chains," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 3(4), pages 40-57, October.
  • Handle: RePEc:igg:jiit00:v:3:y:2007:i:4:p:40-57
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    Cited by:

    1. Mohamad Ghozali Hassan* & Che AzlanTaib & Muslim Akanmu & Afif Ahmarofi, 2018. "A Theoretical Review on the Preventive Measures to Landslide Disaster Occurrences in Penang State, Malaysia," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 753-759:6.

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