The Future of Control Rooms: Digital Twin and AI

18 June, 2025

In a context of increasing automation in port terminals, control rooms face the challenge of adapting to an increasingly complex and dynamic operation. The article explores how the combination of technologies such as Digital Twin and Artificial Intelligence is redefining the role of operators, enabling them to shift from manual supervision to strategic decision-making based on real-time data.

One of the main challenges identified is the lack of organizational evolution in parallel with technological progress. Automation has increased the complexity of systems, but it has not always been accompanied by a transformation in processes and human roles. The document proposes a human-centered design, where AI supports operational expertise by facilitating exception management and anticipating disruptions.

Data standardization and interoperability between systems are key elements for achieving effective integration. Initiatives such as TIC 4.0 have laid the groundwork for seamless communication between platforms, but barriers still persist due to excessive system customization. The implementation of Digital Twins allows for the consolidation of scattered data into a unified interface, improving visibility and reducing the cognitive load on operators.

The article concludes that the future of control rooms lies not only in automating existing processes, but in transforming the way operators interact with technology. This evolution paves the way for a new era of operational intelligence, where decision-making becomes more agile, accurate, and resilient in the face of the variability of the port environment.

“By leveraging real-time Digital Twin technology, terminals gain acute visibility into inefficiencies, unlocking a new era for port and logistics operations.”

The main inputs and contributions are:

• Automation has improved operational discipline in terminals, but it has not resolved the complexity of real-world operations, which remain dynamic and reliant on human intervention. The article emphasizes the need to evolve toward a more strategic control room model, where technology complements operator expertise.
• Despite technological advances, many control rooms still operate with multiple disconnected systems, leading to cognitive overload, slow response times, and increased operational risk. This fragmentation limits efficiency and hinders real-time decision-making.
• The lack of interoperability between systems such as TOS, ECS, or IoT platforms remains a significant obstacle. Although initiatives like TIC 4.0 promote standardization, many terminals still rely on customized integrations that are neither scalable nor replicable.
• It proposes a user-centered approach, where Artificial Intelligence and Digital Twin technologies consolidate data into a single visual interface. This facilitates exception management, anticipates disruptions, and enhances operational responsiveness.
• Traditional continuous improvement based on LEAN methodologies is strengthened by the integration of AI, which enables the automation of anomaly detection, root cause diagnosis, and the recommendation of corrective actions. This evolution allows a shift from reactive management to proactive optimization.
• The operator experience is transformed through intuitive interfaces that integrate historical and real-time data, reducing the need to navigate across multiple systems and improving operational efficiency.
• It positions control rooms as intelligent hubs of logistics orchestration, capable of adapting to environmental variability and continuously evolving through the use of data and advanced technology.
• It highlights that the evolution of control rooms depends not only on technology, but also on cultural and organizational transformation. To ensure effective adoption of solutions like AI and Digital Twin, it is essential to foster a mindset of continuous improvement, train operators in the use of new tools, and promote a culture of collaboration across departments and systems.

“AI-powered continuous improvement is not just about efficiency gains—it creates self-optimising terminals where operational intelligence evolves in real time.”