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Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Hassan Younis (School of Management and Logistic Sciences, German Jordanian University, Amman, Jordan)
Balan Sundarakani (Faculty of Business, University of Wollongong in Dubai, Dubai, United Arab Emirates)
Malek Alsharairi (School of Management and Logistic Sciences, German Jordanian University, Amman, Jordan)

Journal of Modelling in Management

ISSN: 1746-5664

Article publication date: 30 August 2021

Issue publication date: 22 August 2022




The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof.


Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM).


AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness.

Research limitations/implications

This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue.

Practical implications

This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore.


Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.



The authors would like to acknowledge the comments provided by the Editor-in-Chief, Guest Editors and Editorial Reviewers, which enhanced the quality of the paper considerably.


Younis, H., Sundarakani, B. and Alsharairi, M. (2022), "Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions", Journal of Modelling in Management, Vol. 17 No. 3, pp. 916-940.



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