An AI-Based Decision Intelligence Framework for Autonomous Logistics Planning in Emerging Markets

Autonomous logistics planning is rapidly gaining traction in emerging markets, driven by the need for efficiency, scalability, and resilience in the face of infrastructure limitations and volatile demand patterns. This paper explores the development of an AI-based decision intelligence framework to enhance autonomous logistics planning, focusing on integrating predictive analytics, reinforcement learning, and real-time data processing for route and fleet optimization. Using a systematic literature review methodology, this study synthesizes global best practices and existing frameworks to propose a scalable, adaptable solution suitable for the unique constraints of emerging markets. The framework addresses last-mile delivery challenges, resource allocation, and predictive demand management while aligning with sustainability goals and digital transformation strategies in logistics operations.