ItalyManufacturing2024 – ongoingFerrari
Machine learning for Formula 1 power-unit assembly analysis
Ferrari reduced assembly analysis time by 50% with an AWS-supported system, AWS reported
Problem
Ferrari engineers manually analyzed power-unit assembly data spread across separate systems. Small assembly deviations could contribute to component failures.
Approach
Ahead of the 2024 season, Scuderia Ferrari HP worked with AWS on a shared data lake and a SageMaker machine-learning pipeline. Engineers used QuickSight dashboards to monitor assembly deviations and investigate anomalies.
Results
Takeaway
The reported gains covered the combined data and analysis system. AWS did not isolate the contribution of machine learning.
Categories
- Industry
- Manufacturing
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Partner co-development
- Evidence
- Vendor case study
Sources
Source summaries
AWS described Ferrari's assembly monitoring pipeline and dashboards. It reported an average 50% reduction in analysis time and processing of at least 4 times as much data.
Full details are in the original sources.
