Manufacturing and Technical Service Practices to Improve Efficiency Through AI-Powered Technology
The three examples have all gone beyond the proof of concept (PoC) stage and have been successfully deployed at an industrial scale. The use cases have been applied in industries — automotive, machinery, and crop nutrition production — that are poised to gain significantly from AI- and ML-based solutions. IDC's focus is on innovation and the added value that industrial users can gain from such solutions."AI-based models are uniquely useful tools that can be deployed in complex production environments. Compared to common analytical tools, AI-powered technology can more easily amplify correlations between different parameters in complicated production and service environments that generate large volumes of structured or unstructured data. These benefits are making AI a cornerstone of organizations' digital transformation journeys." — Senior Director Jan Burian, Head of IDC Manufacturing Insights EMEA
Please Note: Extended description available upon request.
- IDC PeerScape Figure
- Executive Summary
- Peer Insights
- Practice 1: Real-Time Quality Control and Anomaly Detection at Skoda Auto
- Challenge
- Example
- Guidance
- Practice 2: Improving Service Levels at Combilift Via an AI-Powered Solution
- Challenge
- Example
- Guidance
- Practice 3: Using an Energy Prediction Model Across Yara's Ammonia Plants to Manage Energy Efficiency
- Challenge
- Example
- Guidance
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