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Predictive Big Data Analytics for Supply Chain Demand Forecasting: Methods, Applications, and Research Opportunities

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Predictive Big Data Analytics for Supply Chain Demand Forecasting: Methods, Applications, and Research Opportunities

Seyedan, Mahya and Mafakheri, Fereshteh ORCID: https://orcid.org/0000-0002-7991-4635 (2020) Predictive Big Data Analytics for Supply Chain Demand Forecasting: Methods, Applications, and Research Opportunities. Journal Of Big Data . ISSN 2196-1115

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Official URL: https://doi.org/10.1186/s40537-020-00329-2

Abstract

Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Article
Refereed:Yes
Authors:Seyedan, Mahya and Mafakheri, Fereshteh
Journal or Publication:Journal Of Big Data
Date:12 July 2020
Digital Object Identifier (DOI):10.1186/s40537-020-00329-2
Keywords:Demand Forecasting, Supply Chain Management, Closed-loop Supply Chains, Big Data Analytics, Machine-learning
ID Code:987759
Deposited By: FERESHTEH MAFAKHERI
Deposited On:02 Feb 2021 22:55
Last Modified:02 Feb 2021 22:55
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