Port capacity is experiencing increasing strain as congestion elevates costs, disrupts supply chains, and impedes economic activity. Nearly 90% of global trade continues to rely on ports, many of which are increasingly challenged by demand imbalances, operational inefficiencies, and inadequate infrastructure.
Additional pressures arise from weather disruptions, labour shortages, and constraints, including insufficient cranes, limited yard space, and inadequate landside connections.
The consequences of port congestion are extensive.
Recent analysis by S&P Global shows a global decline in port efficiency, marked by fewer moves per hour, longer processing times, and longer port stays across major regions.
Optimising port traffic flows with AI
In this environment, visibility becomes critical. Without accurate, real-time insights, vessels fall into the costly pattern of “rush to wait”- speeding toward ports only to sit idle offshore. This is where AI-driven voyage intelligence is beginning to reshape operations.
By integrating real-time data on weather, vessel performance, traffic, and port conditions, shipping lines can more accurately predict arrival times and enhance planning. Vessels can adjust speed in advance, synchronise with berth availability, and arrive precisely when needed.
The effects are immediate: waiting times are reduced or eliminated, turnaround efficiency increases, and fuel consumption decreases by 5 to 8 percent, resulting in lower costs and emissions.
Beyond individual voyages, predictive estimated time of arrival (ETA) systems are evolving toward an ‘air traffic control’ model for ports. Comprehensive visibility into incoming vessels enables ports to allocate pilots, labour, and equipment more effectively and to coordinate efficiently with landside logistics.
Ultimately, this transition is both straightforward and impactful: improved information enables more precise timing, which in turn enhances operational efficiency.
