An AI-Driven Decision Automation Framework for Cargo Consolidation in High-Volume Trade Hubs

Abstract

The growing complexity of global supply chains and the expansion of high-volume trade hubs have increased operational demands on cargo consolidation companies. These organizations are required to manage large volumes of shipments while ensuring accurate documentation, efficient space utilization, and timely decision-making. Despite ongoing digital transformation efforts in logistics, many cargo consolidation processes remain heavily reliant on manual intervention or loosely integrated information systems. This dependence often results in operational inefficiencies, delays, and elevated error rates, particularly in environments characterized by high variability and regulatory complexity.
This study presents the design and implementation of an artificial intelligence driven automation framework aimed at enhancing decision support in cargo consolidation operations. The proposed framework integrates advanced data processing, intelligent decision logic, and automated execution mechanisms to support critical consolidation activities, including documentation validation, cargo space optimization, and real-time operational prioritization. A mixed-methods research approach is employed to evaluate the framework, combining qualitative insights from logistics professionals and artificial intelligence specialists with quantitative analysis of key operational performance indicators such as processing time, documentation accuracy, and resource utilization efficiency.
The results demonstrate that the adoption of AI-driven automation leads to significant improvements in decision accuracy, operational speed, and overall efficiency within cargo consolidation workflows. In addition to performance gains, the framework enhances operational resilience and scalability, supporting modern logistics transformation initiatives. By addressing an underexplored area of supply chain automation, this research contributes a practical and transferable framework that advances both academic understanding and industry practice in intelligent logistics systems operating within high-volume trade hubs.