Physical AI connects the digital and real worlds. Instead of just analysing data in the background, planning processes, and issuing warnings for problems, artificial intelligence can intervene directly and control manufacturing systems. This enables an unprecedented level of automation because the systems are capable of reacting to new and unknown situations and learning from their experiences. NTT Data has identified eight key areas where Physical AI offers the greatest added value.
A completely new chapter is being opened in manufacturing automation with AI and robotics, as it is no longer limited to highly standardised processes. Even complex tasks and processes with high variability can be automated ever more effectively, and unforeseen events no longer throw manufacturing off track. The key is Physical AI, which allows intelligent control of physical systems in real-time, revolutionising their adaptability. Its use is particularly promising in the following areas:
Intralogistics for small and small-batch production
The manufacturing of products in small quantities or as custom single pieces presents logistical challenges. Physical AI enables the deployment of autonomous mobile robots (AMRs) that can independently pick and transport parts. These robots optimise their routes, navigate around obstacles, and prioritise urgent orders. Through the continuous learning process of the AMRs, companies can shorten lead times and improve on-time delivery, while reducing the need for warehouse inventory.
Individual machine control
Small-batch production often requires frequent adjustments to machines. Physical AI enables these adjustments to be made dynamically by ordering and processing the correct materials. For example, a gripper can recognise what is currently in front of the machine and can grab it precisely – without knocking over or damaging parts, even if they are new to it. This technology reduces setup times and increases manufacturing flexibility without manual intervention.
Maintenance optimisation
Predictive maintenance becomes more reliable with Physical AI. By integrating sensor data, AI can accurately predict wear and defects. So-called sensor fusion enables a comprehensive picture of machine conditions to be obtained. Machine parameters can be automatically adjusted or machines can be stopped to prevent damage. The scheduling of maintenance windows is also done autonomously to minimise downtime.
Automated quality control
Systems equipped with AI and cameras significantly improve quality control. They detect the smallest defects and sort out faulty products. These systems deliver better results than traditional vision systems and can be integrated directly into the production process, thereby reducing rejection rates.
Self-optimising machines
Machines that self-optimise combine the benefits of quality control and maintenance optimisation. They adjust manufacturing parameters based on current tool conditions and initiate necessary measures to maintain production quality. This technology also shortens the ramp-up phase for new products by incrementally optimising manufacturing parameters.
Autonomous monitoring and inspection
Drones and robots autonomously monitor production facilities and react to irregularities. Equipped with infrared and LiDAR technology, they can also work precisely in poor lighting conditions. They initiate measures such as shutting down machines or closing valves to minimise safety risks.
Human-machine cooperation
Physical AI improves collaboration between humans and machines by enabling robots to recognise and react to human movements and gestures. This allows for safe and efficient interaction without safety guards. Robots assist with ergonomically demanding tasks and deliver needed parts just in time.
Humanoid robots
Humanoid robots are increasingly taking on complex tasks in manufacturing. They are particularly useful where skilled workers are scarce or in repetitive tasks. Their ability to move freely and support various processes makes them a valuable component of modern manufacturing facilities.
What decision-makers should consider
Physical AI requires comprehensive AI skills and seamless integration of IT and OT systems. Companies should initially test the technology in pilot projects and then standardise it.
„Intelligent machines that can not only think but also act are the future of manufacturing. They can help manufacturing companies mitigate the shortage of skilled workers, optimise complex manufacturing processes, and even automate operations that previously seemed unautomatable. Those who sleep on this topic will likely struggle to remain competitive in the long term.“
Oliver Köth, Managing Director Technology & Innovation, NTT Data DACH
When implemented carefully, Physical-AI projects deliver significant added value. Typical results, based on NTT Data's experience, include a 20 to 40 percent reduction in downtime, a 10 to 25 percent increase in Overall Equipment Effectiveness (OEE), 15 to 30 percent less waste produced, and significantly shorter ramp-up times for manufacturing new products.
SourceNTT Data
