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IJML 2023 Vol.13(2): 70-76 ISSN: 2010-3700
DOI: 10.18178/ijml.2023.13.2.1131

Big Data Applications in Supply Chain Management: SCOPUS Based Review

Baha M. Mohsen

Manuscript received February 12, 2022; revised May 17, 2022; accepted December 11, 2022.

Abstract—For modern industry, supply chain optimization is becoming very important. To stay ahead of the competition, companies must be able to optimize their supply chain. Customers expect fast order fulfilment and delivery, as well as product options, styles and features. Companies that meet these expectations are expected to succeed. Big Data plays an important role in various areas of supply chain management, such as demand forecasting, product development, delivery decisions, sales and customer feedback. The increasing amount of data shared by supply chains in manufacturing and service sectors justifies the use of Big Data in supply chain management. This paper reviews research activities in the area of Big Data in supply chain management. It also examines the applications of Big Data in supply chain management, opportunities, challenges and future trends.

Index Terms—Big data, supply chain management, SCOPUS

Baha M. Mohsen is with Wayne State University, Industrial and Systems Engineering Department, Detroit, MI 48202 USA


Cite: Baha M. Mohsen, "Big Data Applications in Supply Chain Management: SCOPUS Based Review," International Journal of Machine Learning vol. 13, no. 2, pp. 70-76, 2023.

Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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