They landed the drone on the turbine’s nacelle platform with two minutes to spare. Hans and his team, guided by the AR headset (powered by for ultra-low latency), replaced the bearing in a record 47 minutes.
Hans, the shift lead, groaned. “Manual? Anja, that means we need the full maintenance history, the spare part bin location, and the step-by-step overhaul protocol. The SAP GUI is crawling like a frozen slug.”
Behind the scenes, AWS functions triggered a Amazon SageMaker model. The model ingested five years of vibration data from the turbine’s IoT sensors, which was stored not on a slow hard drive in Hamburg, but in Amazon S3 —the petabyte-scale storage lake.
“Because we’re not using batch updates anymore,” she said. She showed him her screen. An ETL job had just extracted the inventory data from the warehouse RFID readers, transformed it, and loaded it into SAP PM in real time . The bin was accurate. Plant Maintenance With Sap Practical Guide Aws
The problem wasn’t the bearing. It was the data .
That night, back on shore, the CFO called.
Anja looked at a live 3D model of Turbine 7. The bearing was highlighted in red. She zoomed in. The model, stored in S3 and rendered by , showed her exactly which bolt needed loosening first. They landed the drone on the turbine’s nacelle
The CFO was silent.
Then came the magic of .
“Sir,” she said, smiling, “that €300 included the compute for the AI prediction, the storage for the digital twin, the drone integration, and the real-time inventory sync. On our old servers, that would have cost €15,000 and taken three days.” “Manual
The next morning, Anja ran a report: . But she didn't run it on SAP. She ran it on Amazon QuickSight , which queried the SAP data in S3. The dashboard showed a 99.99% uptime for the quarter.
Anja watched the drone’s telemetry stream into a topic, which fed back into SAP PM. The maintenance order status updated automatically: “Spare part in transit. ETA: 18 minutes.”