Case Study: Fagor Ederlan detects defects sooner and boosts automotive parts quality with Microsoft Azure

Developers use cloud power to boost quality of automotive parts manufacturing

Fagor Ederlan, a Spanish automotive-parts manufacturer, needed to detect defects in aluminum steering and brake components at the moment they’re produced. The company faced a mix of legacy and proprietary machines, millisecond-rate sensor CSV files, limited network bandwidth, and a one-month delay before customer quality feedback, making early detection and centralized analysis difficult.

A five-day hackfest produced a cloud prototype using Azure IoT Hub, Azure Functions, Azure Stream Analytics, Azure Machine Learning, and Power BI: a lightweight Windows Service compresses and uploads CSVs, Functions decompress and extract features, Stream Analytics joins data and calls ML models, and dashboards show per-piece results in near real time. The solution reduced network load with zip management, enabled retrainable ML models for earlier defect identification, and can be scaled and applied across Fagor Ederlan’s machines and factories.

Fagor Ederlan