Heavy industry relies on people to inspect hazardous, dirty facilities. It’s expensive, and putting humans in these zones carries obvious safety risks. Swiss robot maker ANYbotics and software company SAP are trying to change that.
ANYbotics’ four-legged autonomous robots will be connected straight into SAP’s backend enterprise resource planning software. Instead of treating a robot as a standalone asset, this turns it into a mobile data-gathering node within an industrial IoT network.
This initiative shows that hardware innovation can now effectively connect with established business workflows. Underscoring that broader trend, SAP is sponsoring this year’s AI & Big Data Expo North America at the San Jose McEnery Convention Center, CA, an event that is fittingly co-located with the IoT Tech Expo and Intelligent Automation & Physical AI Summit.
When equipment breaks at a chemical plant or offshore rig, it costs a fortune. People do routine inspections to catch these issues early, but humans get tired and plants are massive. Robots, on the other hand, can walk the floor constantly, carrying thermal, acoustic, and visual sensors. Hook those sensors into SAP, and a hot pump instantly generates a maintenance request without waiting for a human to report it.
Cutting out the reporting lag
Usually, finding a problem and logging a work order are two disconnected steps. A worker might hear a weird noise in a compressor, write it down, and type it into a computer hours later. By the time the replacement part gets approved, the machine might be wrecked.
Connecting ANYbotics to SAP eliminates that delay. The robot’s onboard AI processes what it sees and hears instantly. If it hears an irregular motor frequency, it doesn’t just flash a warning on a separate screen, it uses APIs to tell the SAP asset management module directly. The system immediately checks for spare parts, figures out the cost of potential downtime, and schedules an engineer.
This automates the flow of information from the floor to management. It also means machinery gets judged on hard, consistent numbers instead of a human inspector’s subjective opinion.
Putting robots in heavy industry isn’t like installing software in an office—companies have to deal with unreliable infrastructure. Factories usually have awful internet connectivity due to thick concrete, metal scaffolding, and electromagnetic interference.
To make this work, the setup relies on edge computing. It takes too much bandwidth to constantly stream high-def thermal video and lidar data to the cloud. So, the robots crunch most of that data locally. Onboard processors figure out the difference between a machine running normally and one that’s dangerously overheating. They only send the crucial details (i.e. the specific fault and its location) back to SAP.
To handle the network issues, many early adopters build private 5G networks. This gives them the coverage they need across huge facilities where regular Wi-Fi fails. It also locks down access, keeping the robot’s data safe from interception.
Of course, security is a major issue. A walking robot packed with cameras is effectively a roaming vulnerability. Companies must use zero-trust network protocols to constantly verify the robot’s identity and limit what SAP modules it can touch. If the robot gets hacked, the system has to cut its connection instantly to stop the attackers from moving laterally into the corporate network.
These robots generate a massive amount of unstructured data as they walk around. Turning raw audio and thermal images into the neat tables SAP requires is difficult.
If companies don’t manage this right, maintenance teams will drown in alerts. A robot that is too sensitive might spit out hundreds of useless warnings a day, making the SAP dashboard completely ignored. IT teams have to set strict rules before turning the system on. They need exact thresholds for what triggers a real maintenance ticket and what just needs to be watched.
The setup usually uses middleware to translate the robot’s telemetry into SAP’s language. This software acts as a filter, throwing out the noise so only actual problems reach the ERP system. The data lake storing all this information also needs to be organised for future machine learning projects. Fixing broken machines is the short-term goal; the long-term payoff is using years of robot data to predict failures before they happen.
Ensuring a successful physical AI deployment
Dropping robots into a factory naturally makes people nervous. The project’s success often comes down to how human resources handles it. Workers usually look at the robots and assume layoffs are next.
Management has to be clear about why the robots are there. The goal is to get people out of dangerous areas like high-voltage zones or toxic chemical sectors to reduce injuries. The robot collects the data, and the human engineer shifts to analysing that data and doing the actual repairs.
This requires retraining. Workers who used to walk the perimeter now have to read SAP dashboards, manage automated tickets, and work with the robots. They have to trust the sensors, and management has to make sure operators know they can take manual control if something unexpected happens.
Companies need to take the rollout slowly. Because syncing physical robots with enterprise software is complicated, large-scale rollouts should start as small, targeted pilots.
The first test should be in one specific area with known hazards but rock-solid internet. This lets IT watch the data flow between the hardware and SAP in a controlled space. At this stage, the main job is making sure the data matches reality. If the robot sees one thing and SAP records another, it has to be audited and fixed daily.
Once the data pipeline actually works, the company can add more robots and connect other systems, like automated parts ordering. IT chiefs have to keep checking if their private networks can handle more robots, while security teams update their defenses against new threats.
If companies treat these autonomous inspectors as an extension of their corporate data architecture, they get a massive amount of information about their physical assets. But pulling it off means getting the network infrastructure, the data rules, and the human element exactly right.
See also: The rise of invisible IoT in enterprise operations
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