On July 3, 2025, the “2025 Smart Port Technology & Industry Global Forum” was held at BPEX(Busan Port Int'l Exhibition & Convention Center) in Busan, jointly hosted by the Korea Maritime Institute (KMI) and the International Association of Ports and Harbors (IAPH). At this event, key industry leaders including Oscar Pernia, CTO of NextPort.Ai, and Tommi Rinta-Kartano, Senior Manager at Kalmar, shared their insights on future port technologies and operational strategies. Among the notable presentations was “Integrated Smart Solutions for the Next-Generation Port” by Lucy Lee, Senior Consultant at CyberLogitec (CLT), which is summarized here.

This session emphasized a crucial shift in focus: from smart terminals to smart ports—and from current systems to next-generation solutions. The term “next-generation” is often used in two contexts: when an industry reaches the limit of its efficiency gains, or when rapid environmental change renders existing systems obsolete. The port industry today faces both.

The Wave of Change in Port Operations

Ports around the world are confronting the dual challenges of legacy system limitations and sweeping shifts in the global environment. For example, data collected from multiple port authorities reveals that fully automated terminals saw a 30% increase in throughput over the past year, while conventional terminals experienced a 1.9% decrease. Profitability for automated terminals also rose by 22%, demonstrating their operational and financial superiority.

Meanwhile, the rapid upscaling of vessels continues. Orders for ships exceeding 20,000 TEU are surging, and shipping alliances now demand berth productivity equivalent to 31 cranes or more. Yet most individual terminals—aside from a few mega-hubs—struggle to accommodate such scale, resulting in transshipment cost burdens and weakened port competitiveness. Berthing delays caused by infrastructure shortages further drive the need for collaboration and integrated operations among terminals.

In this context, ports require an operational paradigm that goes beyond legacy automation and isolated optimization. They must build a connected digital ecosystem—centered on automation, intelligence, and interoperability.

Core Technologies for a Digital Port Ecosystem

The technologies enabling this transformation fall into two main categories: (1) automation and infrastructure technologies, and (2) cognitive technologies that leverage the data generated by automation.

For instance, unmanned equipment movements are transmitted via 5G to an IoT platform, which collects and analyzes data in real time—enabling machine-based cognition rather than human-driven monitoring.

Equipment Automation and AMR Adoption

Significant advances have already been made in the automation of terminal equipment, especially in horizontal transport. Traditional manned vehicles have evolved into AGVs guided by transponders, and now into autonomous mobile robots (AMRs) equipped with multi-sensors for environmental awareness. A key benefit of AMRs is their ability to operate safely in mixed traffic with manual equipment or external trucks, allowing for leapfrogged deployment without intermediate infrastructure.

Currently, multiple terminals including Busan New Port are conducting AMR pilot programs. Globally, many ports (including Busan) have achieved full automation, while others like Ningbo and Shenzhen remain partially automated. In some regions—especially India and Latin America—retrofit automation is gaining traction, enabling ports to upgrade existing equipment (e.g., remote STS and yard cranes, new horizontal transport units) into fully automated systems.

Such automation demands infrastructure support. As shown in the referenced chart, more than 50 messages per second are exchanged between terminal systems and equipment—not including inter-equipment communication. This level of messaging requires high-speed, ultra-reliable network infrastructure. Among available options, 5G outperforms Wi-Fi and wired networks in mobility, coverage, and security, making it foundational for digital port transformation.

Data-Driven Optimization with IoT and AI 

5G-based communication enables real-time control between automated systems and a centralized IoT platform, which comprises rule-based control, event monitoring, analytics engines, and a data center—laying the groundwork for data-driven decision-making.

In port operations, bottlenecks at a single point often impact the entire workflow. Data from a six-month operational analysis revealed that a one-hour vessel delay reduced crane productivity by 8%, and a two-hour delay by 14%. To address such inefficiencies, Digital Twin technology replicates the physical port in a virtual space for real-time monitoring and operational simulation. Combined with AI, Digital Twins can go beyond visualization and support predictive simulations.

 The Role of AI in Real-Time Optimization 

Conventional TOS (Terminal Operating Systems) include optimization logic, but these are based on fixed rules and limited adaptability. Port operations are dynamic, with constant changes in equipment status and work schedules—making real-time optimization difficult with traditional systems. AI, however, excels in processing complex variables and providing predictive insight.

While AI can analyze and forecast data, it still requires execution through connected systems. This is where intelligent integrated control systems come into play—serving as the neural network that executes AI decisions across IoT platforms, automation equipment, and sensors.

Future Vision of Port Operations 

In the future, terminals will be operated with fully automated, 5G-connected equipment, enabling unmanned, around-the-clock operations with minimal human intervention. AI-enabled platforms will optimize container yard planning, berthing schedules, and maintenance forecasting.

Operators will be able to simulate operations via virtual terminals, pre-test scenarios, and detect equipment failures in advance. These AI-driven routines will create a predictive operations model, shifting ports from reactive to proactive management.

While full automation is already maturing globally, AI-driven operations are still in early stages. CLT is currently conducting a proof-of-concept project that demonstrates its real-world potential.

 A Real-World AI Case: Dwell Time Optimization

One global terminal (with over 10 million TEU annual throughput) was facing critical yard congestion. CLT developed an AI service to predict container dwell time and optimize yard placement.

1.    A machine learning model forecasts dwell time based on container history.

2.    The AI system uses this prediction and TOS operational factors to assign yard positions.

3.    These assignments feed into a continuous learning loop—Plan → Execute → Learn → Replan—improving prediction accuracy over time.

The POC aims to reduce unnecessary yard shifting by up to 30%. The AI prediction model:

·         Avoids concentration in specific blocks,

·         Simulates bottlenecks pre-execution,

·         Provides a real-time dashboard for anomaly detection and performance monitoring.

This AI service is offered as an add-on to existing TOS systems and can also be applied in multimodal environments, where seamless data exchange is essential across stakeholders like carriers, inland transport, and customs. Instead of full system integration, CLT proposes a lightweight data linkage model using asynchronous messaging and APIs—preserving system independence while enabling intelligent ecosystem-level optimization.

 

Building a Digital Ecosystem

The future of smart ports lies in creating a digital ecosystem—built not only on automation and intelligence, but on scalable, interoperable, and add-on solutions. An ecosystem thrives when participants are organically connected in a self-reinforcing structure, sharing both technology and value.

As a key player in the smart port ecosystem, CyberLogitec is committed to continuous collaboration and value creation through future-ready solutions.

 

 

Author: Lucy Lee, Terminal Business Consultant, CyberLogitec
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