Over the last decades, supply chain management has undergone a profound transformation, driven not only by rapid technological advancements but also by the increasing complexity and globalization of markets, the growing volatility of demand, and the emergence of new competitive, environmental, and institutional pressures that require greater efficiency, transparency, and adaptability. In this context, Artificial Intelligence (AI) has emerged as a key enabler of supply chain (SC) transformation, offering significant improvements in decision-making processes the optimization of the operations the enhancement of efficiency, resilience and sustainability. Despite the growing academic and managerial interest, the literature on AI in SCs remains fragmented across disciplines, sectors, and application domains.
This study aims to provide a systematic and integrated overview of the role of AI in SCs by identifying its main areas of application, emerging research trajectories, and key challenges associated with its adoption. To achieve the results, the research adopts a systematic literature review approach configured as a meta-review, focusing exclusively on review-type articles. The study adopts a systematic literature review approach configured as a meta-review, based on review articles retrieved from the Scopus database. The analysis integrates descriptive statistics with bibliometric techniques supported by VOSviewer.
The results reveal a non-uniform distribution of research, with a strong concentration in specific application domains, particularly agri-food, Supply chain-related areas such as logistics and distribution. AI applications in these domains primarily focus on demand forecasting, process optimization, traceability, and quality control. Emerging trends highlight the increasing relevance of sustainability, resilience, and digital integration, supported by complementary technologies such as blockchain, Internet of Things (IoT), and big data. At the same time, significant gaps persist, including the uneven maturity of AI applications across sectors, with some areas showing advanced implementation while others remain at an early stage. Limited technological integration, and insufficient attention to ethical and governance issues emerged.
Overall, the findings suggest that the main challenge is not the lack of interest in AI, but the need for strategies to fully exploit the potential of these technologies in managing various aspects of the SC. Based on this consideration, further research is needed to develop approaches capable of addressing the adoption of AI.