Global supply chains are increasingly challenged by trade disruptions, regulatory fragmentation and operational risk, necessitating innovative solutions such as Blockchain and artificial intelligence (AI). This study systematically aims to examine how Blockchain–AI integration transforms supply chain management (SCM) by analyzing its impact across key drivers (facilities, inventory, transportation, information, sourcing and pricing) while identifying research gaps and adoption challenges.
The authors combined a systematic literature review (SLR) and a case study to examine how Blockchain characteristics (immutability, transparency and consensus) and AI characteristics (accurate data extraction, efficient data analysis and automation through human-logic substitution) intersect with SCM drivers. The literature review findings are complemented by a case study, allowing theoretical insights to be compared with real-world implementation.
Among the six SCM drivers, information is the most studied, leveraging AI’s predictive analytics and Blockchain’s transparency and immutability, whereas facilities and pricing warrant further exploration. The case study of the Ecosystem Orchestration Platform shows how private blockchains and agentic AI can mitigate scalability concerns, while smart contracts address privacy concerns through granular permissions. Based on these findings, the authors present a framework for Blockchain–AI integration based on the task-technology fit model, along with practical guidelines informed by the system’s actual deployment.
This study bridges theory and practice, offering a holistic understanding of Blockchain–AI integration in SCM. By conducting an SLR and examining Company A’s real-world platform deployments, this research provides a nuanced and balanced view that combines academic and empirical insights on the feasibility, benefits and challenges of Blockchain–AI integration in SCM.
