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IIoT Analytics Revolutionizing the Automotive Painting Industry: A Case Study on Dürr
Technology Category
- Sensors - Air Pollution Sensors
- Sensors - Environmental Sensors
Applicable Industries
- Automotive
- Equipment & Machinery
Applicable Functions
- Maintenance
Use Cases
- Manufacturing System Automation
- Predictive Maintenance
Services
- System Integration
The Challenge
Dürr, a leading mechanical and plant engineering firm, faced several challenges in its operations. The company was under pressure to meet customer demand for secure monitoring of production data and self-service predictive maintenance capabilities. There was also a need to avoid painting faults caused by materials or equipment wear. Dürr was also grappling with growing ESG requirements to cut energy use and emissions. The company was in the process of transforming from traditional manufacturing to a digital business, which presented its own set of challenges.
About The Customer
Dürr is one of the world’s leading mechanical and plant engineering firms. Its turnkey paint shops, final assembly systems, and machine and robot technology harness digitalization and Industry 4.0 concepts to power manufacturing across the automotive industry. Dürr generates over €3.5 billion in revenue annually, and its paint shops, used by every major automaker globally, have achieved massive energy consumption savings. The company has been a leader in mechanical engineering since Paul Dürr opened his first metal workshop in 1895 in Germany.
The Solution
Dürr adopted the Cumulocity IoT Platform to address its challenges. The company implemented real-time monitoring for 64 robots, which accelerated customer growth and planned robot purchases. Dürr also co-developed self-service analytics tools for operational engineers, which helped generate new revenue streams from innovative digital services. The system consists of an industrial PC located next to each robotic paint station which has an IIoT edge installation that can control all painting processes, monitor for defects and alert operators to customized triggers. Using self-service predictive maintenance and anomaly detection, air nozzles are now cleaned only when needed to prevent contamination. Defective paint runs due to contaminated shaping air nozzles no longer happen because the new system detects such faults immediately.
Operational Impact
Quantitative Benefit
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