What type of study is this?

This is a Systematic Review study (also classified as: Bibliometric Analysis).

September 27, 2025Open Access

Evolution and impact of artificial intelligence in sustainable supply chain management: Systematic review and bibliometric analysis

SGSara GhouatiChouaib Doukkali University SOSalah OulfarsiChouaib Doukkali University AAAdil El AmriChouaib Doukkali University

Key Points

AI enhances supply chain processes, particularly in demand forecasting and inventory management, ensuring better performance.
Machine learning significantly dominates the research landscape, evidenced by 16 studies focusing on its application in supply chains.
The systematic review highlights that challenges like algorithmic biases and adoption issues for SMEs still persist in AI integration.
Strategically, AI improves supply chain resilience, although its potential with technologies like blockchain remains largely untapped.

Abstract

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.

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