Artificial intelligence (AI) automates and optimises processes and can solve problems that were previously insolvable – for instance, due to low processing capacity. It is already in operation and is closely interlinked with the concept of Industry 4.0. What else should you know about the use of AI in logistics?
The definition of artificial intelligence – AI for short – is disputed. The ability to solve problems independently is often cited as a key feature of intelligence. For this reason, some scientists do not consider machine learning (ML) to be an intelligent process, while others regard ML as a subcategory of AI.
The global economic expectations for artificial intelligence are enormous (source: statista).
A classic example of ML are the various image recognition programmes that detect and categorise patterns. Other kinds of software analyse repetitive tasks and are then able to perform, adjust and optimise these tasks by themselves. At best, Artificial Intelligence is based on its “live template” – in other words, the neural networks that connect to each other in the human brain. Computers connect information in a similar way, for as long as it takes to recognise a pattern from individual strokes.
Agents, as they are called, try out different ways to achieve a defined goal. If there are errors, the process starts over, as “punishment”. If the ideal result is achieved, this serves as “praise” for the system. It saves the way it has discovered and has thus learned for the future. The better way with the better result is also pursued going forward. This is known as reinforcement learning.
What advantages and disadvantages does artificial intelligence have?
As with autonomous driving, humans can no longer make errors since the task is performed by a machine. On the other hand, humans cannot prevent errors when the machine operates autonomously. A balance must be found.
To exploit the full potential of artificial intelligence, a lengthy start-up phase is necessary. First, the problem must be pinpointed and the right algorithm identified, which can cost a lot of time. Yet the machine programmes will learn more and more over time, like in a kind of continuing education.
The acquisition costs can be significant, particularly if the IT infrastructure is not geared towards AI. One solution is collaboration, as is the case with HHLA Sky, which has entered into a partnership with Deutsche Telekom for the 5G campus networks.
With industrialisation, innovation cannot be stopped. Specialist employees must be trained to conduct research and development and to maintain the technology. This leads to the creation of new careers and exciting jobs at HHLA.
Artificial Intelligence is becoming increasingly important in the digitalised transport chain – for instance, for the quick identification of supply bottlenecks. Cargo can then be rerouted and production flows adjusted accordingly. Even quantum computers could potentially be used here since the number of possibilities often overwhelms conventional computers. Digital twin technology is also a key area of artificial intelligence. With the help of this kind of model, processes or systems can be simulated in advance.
At a port terminal, for example, container transporters travel the same route over and over. AI programmes are already optimising routes like these and could control the entire process using self-driving trucks. Other applications examine images and determine whether a container is damaged.
The question of where AI can best be used in the maritime industry is also addressed in our HHLA Talk with Nils Kemme from Hamburg Port Consulting.
At HHLA, AI is improving automated block storage management as part of a number of projects. What is the best way to place containers to optimise capacity utilisation? Reinforcement learning is already being applied at Container Terminal Altenwerder and at Burchardkai.
Artificial intelligence is also already in use at HHLA for predictive maintenance, to optimise maintenance intervals and prevent operational downtime. Our subsidiary METRANS already uses digital twin technology for more efficient train dispatch. Damage to a train can thus be identified more quickly, enabling it to return to operation sooner. You can read the full article about the use of artificial intelligence in train dispatch in our HHLA magazine.
More efficient terminal management and the associated conservation of energy will, of course, ultimately also contribute to our goal of becoming climate-neutral. This applies not just to vehicles but also to daylight-controlled lighting, which can be switched on or off accordingly. More details can be found in the report on our “Balanced Logistics” sustainability programme