Role of a digital twin to improve the design and operations of ports

This article was originally published in Port Technology International on 1 August 2023

Digital Twins are gaining widespread acceptance. The concept is from the union between the Internet of Things and analytics, such as AI and simulation. Industries which are reliant on complex physical systems and processes are the early adopters. It’s therefore not surprising that this up-and-coming technology is of interest to those charged with designing, operating, and improving ports. But before going there, let’s cut through the hype to define a digital twin, explore how it can deliver value, and give some practical advice on how to best make it work for you. 

A digital twin is an accurate virtual representation of an actual or intended physical object, process, or system. Existing purely in the digital realm, it can be updated using data from its physical counterpart and other sources and employ machine learning and simulation to improve planning and decision-making.  

Applications for digital twins are diverse. Airbus Industries developed a digital twin of its wide-body A380 airliner. By accurately simulating the aircraft's in-flight behaviour in near-real-time, engineers were able to optimise its design and predict maintenance needs – reducing its weight by several tonnes and extending its service life.  

Singapore created a digital twin of its water system to improve the way it manages water for its 5.6 million inhabitants. The twin simulates the water system in real-time, allowing the operators to reduce water loss, increase the efficiency of the island’s water treatment plants, and optimise the island’s water distribution network. 

Digital Twin ports 

The opportunity for ports is huge. A Smart Mooring digital twin in the Port of Rotterdam, warns the port operator of unsafe situations for moored ships. It predicts mooring forces and calculates ship motions days in advance by combining the dynamics of moored vessels with port data and weather forecasts.  

Now imagine a digital twin scaled to an entire port – providing information for making smarter decisions on the performance of individual machines and cranes, the efficiency of bunkering, the speed, flow, and volume of container traffic, and a lot more. Operators can reduce their running costs by fine-tuning real-world operations and this can by simulated against different scenarios. Furthermore, to the delight of the finance department, the benefits of a particular infrastructure investment can be scrutinised before a single penny is committed to a port digital twin. 

Avoiding the pitfalls 

Nearly all digital twins are driven by the need to improve efficiency and solve problems more intuitively, and a digital twin port is no different. As with any IT project, good planning is a perquisite to success. The following advice is worth noting. 

1. Define the problem to be solved

Ensure it is aligned with your business strategy. Be value-driven not technology driven and base it around a sound business case – whether it is to optimise a particular process, screen an investment, reduce downtime, improve a design, or enhance safety. Whatever it is define the issue from the outset and know what success looks like.  

2. Map the different stakeholders and sponsors

This is important as it is likely that people will have different budgets and agendas. Identify the different needs and expectations and get buy-in and consensus upfront. Communicate expectations on what can and cannot be done. Form a common understanding and be inclusive, using language that us common to business and maritime audiences. Don’t forget to address cyber security. 

3. Start off small and scale

Don’t go too far too early, and too fast. Software is expandable and you are better off scaling up your project when you are demonstrating value.  

4. Select the right partner

Ideally, pick one with skills and experience you don’t have. They are more able to see and unlock opportunities you cannot.

5. Select the data sources that matter

Don’t be swamped by data. Gather data from sensors, machines, or other devices that matter most. The data must be accurate, reliable, and most important of all, relevant to the issue being solved. Next, develop your twin, integrate the data, then test and validate it rigorously. Correct inaccuracies and anomalies before you deploy. 

6. Continuously calibrate your twin to improve its accuracy

Improvement is key to ensuring that your digital twin continues to provide value and remains relevant over time.

7. Don’t be afraid to fail. But learn fast and move forward.

As with any emerging technology, different approaches to implementation have emerged. Some vendors are hardware and software-led, others asset and software-based, and there are many niche applications.  

Regardless of the approach, it’s important to remember that success in the virtual world is entirely dependent on the value delivered in the real world. Keep that in mind and you won’t go wrong.