Perkembangan Artificial Intelligence (AI) telah mendorong transformasi dalam Supply Chain Management (SCM) melalui peningkatan efisiensi operasional, kualitas pengambilan keputusan, dan ketahanan rantai pasok. Penelitian ini bertujuan untuk mengidentifikasi perkembangan penelitian AI dalam SCM, teknologi AI yang dominan, manfaat dan tantangan implementasinya, serta peluang penelitian pada periode 2021–2026. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan menganalisis sepuluh artikel yang relevan dari berbagai sumber ilmiah. Data dianalisis menggunakan pendekatan narrative synthesis untuk mengidentifikasi pola, persamaan, dan perbedaan hasil penelitian. Hasil kajian menunjukkan bahwa Machine Learning merupakan teknologi AI yang paling banyak diterapkan, terutama pada demand forecasting, inventory management, optimasi logistik, dan manajemen risiko. Selain itu, perkembangan penelitian juga mengarah pada pemanfaatan Deep Learning, Computer Vision, Natural Language Processing, Digital Twin, dan integrasi AI dengan Internet of Things serta Blockchain untuk meningkatkan transparansi, fleksibilitas, dan ketahanan rantai pasok. Meskipun implementasi AI memberikan manfaat yang signifikan, masih terdapat tantangan berupa kualitas data, kesiapan infrastruktur digital, keamanan siber, dan kompetensi sumber daya manusia. Penelitian ini memberikan gambaran mengenai tren perkembangan AI dalam SCM sekaligus menjadi referensi bagi pengembangan penelitian dan implementasi AI pada berbagai sektor industri.
Scalable event-driven architectures are now the focus of enterprise supply chain and logistics research as this information is surfaced from transport assets, warehouses, suppliers, platforms and risk environments at a more frequent rate to allow for faster decision making. This review looks at the concepts of peer-reviewed studies of 2015–2025 that have focused on architectures that have the ability to transform distributed events into traceability, resilience, visibility, and automated coordination. The review of the literature shows that there is no single concept but rather scattered concepts in the domain of scalable event-driven logistics within the fields of Internet of Things (IoT) in logistics, Logistics 4.0, big data analytics, blockchain traceability, multi-agent control and digital supply chain twins. These streams have significant challenges around event capture, real time analytics, decentralized provenance, and disruption response. Key gaps remain in latency benchmarking, cross-enterprise semantic interoperability, governance of shared event streams, and validated architecture-level performance evidence. The field is significant due to the increased reliance on enterprise architectures that extend beyond the organizational boundary that are also responsive, auditable and resilient.