Artificial intelligence for a more efficient port operation
At the upcoming Smart Digital Ports of the Future conference, David Yeo, Founder and CEO of Innovez One will talk on what AI can do for ports now and what to expect it to achieve in the future. AI will increasingly be used on ships to optimise voyages and make vessels more autonomous, but is it ready for action in ports?
The answer is: yes. While some ports are still using whiteboards or spreadsheets, pioneering ports are already using AI to automate and optimise port, tug and pilotage operations. Machine learning, a subset of AI which enables software to “learn” from data without being explicitly programmed, can improve the efficiency and reduce the carbon footprint of these complex operations.
Pilots must be assigned to specific vessel types and sizes depending on their licence, tugboats must be in sufficient numbers for the job, and shuttles must be planned to take the pilots to the correct boarding grounds. AI and machine learning can use GPS and AIS data to track the position of each vessel and the status of jobs in real time. These technologies can learn from historical data to predict the duration of each job based on factors such as the weather conditions, vessel type and level of service required. They can reallocate resources instantly if a vessel’s ETA changes, giving ports the flexibility they need to respond to problems or delays elsewhere in the supply chain.
Ports in Europe and Asia are already benefitting. For example, in Tanjung Priok, the 22nd busiest port in the world, this technology has reduced the overall distance travelled during tug and pilot operations by 20% and slashed average waiting times for visiting ships, from 2.4 hours to around just 30 minutes.
Moving forward, we can imagine a future where the power of AI will be expanded to more areas of port operations. For instance, machine learning has the potential to help optimise berth management, to ensure that ships are allocated to the right berth at the right time.
Berth allocation is a complex puzzle with numerous constraints, such as the vessel size and type, tidal restrictions, the availability of cranes, and the need for offshore power. The puzzle is likely to become even more complex moving forward, as vessels will be powered by different fuels and technologies, making their needs for port services more specific. Algorithms powered by machine learning could learn from a ports’ data to solve these puzzles seamlessly.
Another potential application of AI is the monitoring of port congestion. Algorithms could be trained to assess and predict levels of congestion from aerial images. This could help ports identify critical situations and take early action to ease congestion before it spirals.
Machine learning could also help predict actual vessel arrival times more accurately, supporting “just in time” initiatives that can significantly cut idling times for visiting vessels. Combined with congestion predictions, this could be used to advise ships to slow down and delay their arrivals, which would help reduce congestion and potentially their emissions – supporting smarter and more sustainable shipping.
Ports are experiencing one of the most fundamental transformations of a generation, as they strive to reduce their own emissions and support decarbonisation across broader supply chains. The scale of the challenge could be matched by the growing potential of a new ally in the journey: AI.
You can register for the Smart Digital Ports of the Future conference by following this link.