AI and ML Applications in Supply Chain Management Field: A Systematic Literature Review

The purpose of this study is to explore the areas of Supply Chain Management (SCM) in which Artificial Intelligence (AI) and Machine Learning (ML) are implemented and to identify the main related AI and ML techniques and research gaps for future applications. A systematic literature review methodology was used to analyze selected studies from 2019 to 2023, collected from Scopusindexed journals, and associate AI and ML with SCM. A total of 50 studies were identified, of which 10 studies were selected using pre-identified inclusion criteria. These studies were thoroughly reviewed and analyzed to identify the main AI and ML applications, techniques, and gaps in SCM. The review revealed that there are two main areas of SCM applications that utilize AI and ML: demand forecasting and supply chain (SC) risk management. Each application area within the SCM utilizes different sets of AI and ML techniques. Demand forecasting studies mainly implement artificial neural network (ANN) and time series/ regression ML models, while SC risk management studies discuss the Internet of Things (IoT) and cloud computing and implement classification ML models. The main gaps found in the shortlisted studies are limited data accessibility and the need for employing deep and hybrid models. The limitations of the study are the review size and the scope of AI and ML applications in SCM. Although a few earlier studies touch on AI or ML applications within SCM, this study offers a panoramic view of both AI and ML within the SC field as a whole.