How a leading automotive manufacturer transformed sensor data into a predictive maintenance engine, eliminating unplanned stops and automating SAP workflows.
Line 2 requires maintenance attention.
This case study illustrates a common challenge for manufacturers implementing Industry 4.0. The solution demonstrates our proven approach to predictive maintenance. Is your factory data going to waste? Let’s connect your machines →
An automotive manufacturer had invested millions in 2,800+ IoT sensors, generating 3.2TB of data monthly. Yet, this intelligence was locked in silos, disconnected from their SAP ERP system.
Maintenance teams relied on gut instinct and rigid schedules, while critical machinery failed without warning, despite the sensors silently recording the warning signs.
A hydraulic press responsible for 40% of output failed catastrophically. The result: 14 days of downtime and $2.1 million in losses.
Post-mortem analysis revealed the sensors had detected the issue three weeks prior—but no one was listening.
We deployed Azure IoT Edge to aggregate data from Siemens, Rockwell, and Bosch systems, filtering noise locally and sending clean data to the cloud.
Using Azure Machine Learning, we trained models to recognize vibration and heat patterns that precede failure, achieving 92% prediction accuracy.
We integrated with SAP PM using Azure Logic Apps. Now, an anomaly automatically creates a work order and orders spare parts—zero human delay.
Enterprise-grade architecture for industrial resilience.
Azure IoT
SAP Integration
Machine Learning
Logic Apps
“The gap between IoT sensors and ERP systems is where millions in savings get lost. When we bridge that gap, factories transform. Watching maintenance teams prevent catastrophic failures weeks in advance—instead of scrambling with emergency repairs—never gets old. That is the promise of Industry 4.0 realized.”
Our team combines deep expertise in industrial IoT, cloud architecture, and enterprise integration. They’ve helped dozens of manufacturers unlock the predictive power hidden in their sensor data.
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