Exploring the Intersection of Artificial Intelligence and Machine Learning in Supply Chain Management: A Structured Literature Review

The purpose of this paper was to identify the contributions and applications of Artificial intelligence (AI) and Machine Learning (ML) algorithms to Supply Chain Management (SCM) through a systematic review of the existing literature. The database used to identify the articles was Scopus, and among 454 articles that met the inclusion criteria and were published during 2010–2022, 81 articles were included in the final analysis. The review process was based on a clear framework that consisted of the definition, selection, analysis, and conclusion phases. By distributing the frequency numerically and using bibliometric analysis and network visualization, the trends of SCM research and the most prevalent AI and ML approaches were outlined. The results demonstrated increased research activity after 2019, with AI and ML utilized mostly in production, marketing, market trend analysis, procurement management, demand forecasting, logistics, supplier selection, and supply chain risk management. The study also established that there is a growing tendency to blend Artificial Neural Networks (ANNs) with other AI approaches to improve SCM results. The literature review also highlighted gaps in the existing literature, for example, the lack of real-world implementation studies and insufficient discussion of the impact of AI on the human side of SCM. Based on the findings of this study, it is recommended that future research focus on methodological and application concerns and incorporate AI-based techniques while integrating Supply Chain Risk Management (SCRM) and sustainability perspectives.