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Forecasting Demand in Supply Chain Using Machine Learning Algorithms

Forecasting Demand in Supply Chain Using Machine Learning Algorithms

Nicholas Ampazis
Copyright: © 2015 |Volume: 5 |Issue: 1 |Pages: 18
ISSN: 1947-3087|EISSN: 1947-3079|EISBN13: 9781466678439|DOI: 10.4018/IJALR.2015010104
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MLA

Ampazis, Nicholas. "Forecasting Demand in Supply Chain Using Machine Learning Algorithms." IJALR vol.5, no.1 2015: pp.56-73. http://doi.org/10.4018/IJALR.2015010104

APA

Ampazis, N. (2015). Forecasting Demand in Supply Chain Using Machine Learning Algorithms. International Journal of Artificial Life Research (IJALR), 5(1), 56-73. http://doi.org/10.4018/IJALR.2015010104

Chicago

Ampazis, Nicholas. "Forecasting Demand in Supply Chain Using Machine Learning Algorithms," International Journal of Artificial Life Research (IJALR) 5, no.1: 56-73. http://doi.org/10.4018/IJALR.2015010104

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Abstract

Managing inventory in a multi-level supply chain structure is a difficult task for big retail stores as it is particularly complex to predict demand for the majority of the items. This paper aims to highlight the potential of machine learning approaches as effective forecasting methods for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. For this purpose, we utilize Artificial Neural Networks (ANNs) trained with an effective second order algorithm, and Support Vector Machines (SVMs) for regression. We evaluated the effectiveness of the proposed approach using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during an especially critical for sales season, which is the Christmas holiday season. In our analysis we also integrated data from two other sources of information, namely an aggregator for movie reviews (Rotten Tomatoes), and a movie oriented social network (Flixster). Consequently, the approach presented in this paper combines the integration of data from various sources of information and the power of advanced machine learning algorithms for lowering the uncertainty barrier in forecasting supply chain demand.

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