Kalmar Forecasts the Growth of AI
Juho Vihonen, AI and analytics architect at Cargotec, has stated that “artificial intelligence is already shaping product development”, in Kalmar’s latest blog post.
With AI and machine learning tools already being applied to ports and terminals, through technologies like “remote monitoring”, it is possible that what are now innovations could soon become a “prerequisite” for customers.
According to Vihonen, “Machine learning and artificial intelligence transform data into a monetisable asset, providing a high degree of competitive advantage.”
Examples given by the AI expert include data-driven robotics, a “cost-effective” solution which is expected to improve performance at automated ports and terminals.
Machine learning and AI can solve a whole host of cargo handling problems, as Kalmar has discovered by linking equipment to a gateway solution that connects machines securely to the Kalmar Cloud, collecting operational data.
To leverage this data, as the blog reveals, predictive condition monitoring has been deployed on approximately 1000 Kalmar reachstackers, monitoring telemetry readings to allow for recurring problems and inefficiencies to be detected.
“The potential of artificial intelligence to disrupt comes discreetly in small steps from domains in which it has already shown success, such as object recognition in imaging” says Vihonen.
“This will result in safer terminal and port spaces, where virtually all cargo handling products will operate independently without a human operator.”
Peter Söderberg, Kalmar, discusses eco-efficient terminal operations in a recent Port Technology technical paper
With increased terminal efficiency described as the “key driver” of machine learning deployment, Kalmar has revealed that its new range of reachstackers will significantly improve the eco-efficiency of operations, thereby contributing to a healthy environment.
Vihonen has also observed that future competitiveness in the port and terminal equipment industry will be typified by data exchange between systems, subsystems and open interfaces, many of which will be autonomous.
“This will result in the development of products relying on data sharing and artificial intelligence-enabled predictive analytics for high uptimes,” Vihonen concludes.