This study analyzes the integration of artificial intelligence (AI) in supply chain management through a systematic review and bibliometric analysis of 292 articles (2020–2023) from Scopus. It examines three areas: the evolution of research (RQ1), the impact of AI on processes (RQ2), and its strategic influence (RQ3). The results reveal that machine learning (16 studies) and deep learning (seven studies) dominate, optimizing demand forecasting and inventory management (Rana & Daultani, 2023). Sectors such as the food industry are benefiting from waste reductions thanks to AI (Kumar et al., 2021), while the automotive industry is improving predictive maintenance (Dumitrascu et al., 2020). However, challenges persist: lack of empirical validation, algorithmic biases, and difficulties with adoption by small and medium-sized enterprises (SMEs) (Fosso Wamba, 2022). Strategically, AI strengthens the resilience of supply chains (Christopher & Holweg, 2017), but its hybrid potential (e.g., AI + blockchain) remains underexploited (Arunmozhi et al., 2022). Theoretical implications highlight the need for dynamic models that integrate socioeconomic criteria, while practitioners must adapt AI to specific industry circumstances. This study provides valuable insights to guide researchers and practitioners in leveraging AI technologies to improve supply chain efficiency, resilience, and performance.
Ghouati et al. (Thu,) studied this question.