Application of Artificial Intelligence in performance Supply Chain Management

Authors

  • Youness Gherabi PhD Student, Hassan First University of Settat, Faculty of Economics and Management, Research Laboratory in Economics, Management, Business Management (LAREGMA). Morocco
  • Zakaria Benjouid Professor, Hassan First University of Settat, Faculty of Economics and Management, Research Laboratory in Economics, Management, Business Management (LAREGMA). Morocco
  • El Khalil EL MOUNTASSIR PhD Student, Hassan First University of Settat, Faculty of Economics and Management, Research Laboratory in Economics, Management, Business Management (LAREGMA). Morocco
  • Nadia Nabil PhD Student, Hassan First University of Settat, Faculty of Economics and Management, Research Laboratory in Economics, Management, Business Management (LAREGMA). Morocco

DOI:

https://doi.org/10.5281/zenodo.7964127

Keywords:

Machine Learning; SCM; Artificial Intelligence; Decision making, Demand forecasting.

Abstract

The volume of data generated by the various Supply Chain Management actors is considerable, the extraction, processing and analysis of this data becomes a priority for decision makers to better understand the origin of problems that sometimes cause disruptions in the decision-making process. At the same time, companies find themselves with increased competition, to persist, decision makers must act quickly by analysing their own data and those coming from outside their businesses. Traditional methods have shown their limits in terms of exploration and interpretation of this growing volume of data. The aim of this paper is to highlight the new applied methods of Artificial Intelligence in Supply Chain Management functions, we will identify the most well-known Machine Learning techniques and review the applications of Machine Learning algorithms in Supply Chain Management.

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Published

2023-05-23

How to Cite

Gherabi, Y. ., Benjouid, Z. ., EL MOUNTASSIR, E. K. ., & Nabil, N. . (2023). Application of Artificial Intelligence in performance Supply Chain Management. International Journal of Strategic Management and Economic Studies (IJSMES), 2(3), 755–767. https://doi.org/10.5281/zenodo.7964127

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