Learn how AI augmentation of institutional knowledge can improve output and quality, creating sustainable market dominance. This manufacturer reduces waste, improves yield, and saves $1 million a month conservatively.
Key product line represents ~$100M revenue/yr
- Market dominance relies on high quality product
- Currently discard 25% due to high standards
- Digital cameras used for inspection
- Process engineers still must manually inspect
- Labor intensive
- Error prone
- Process data captured
- Per second temp. in each zone of process
- Combined w/ manual reports from engineers
- Model production lifecycle with digital twin
- Predict # of deformities
- Prescriptive optimizer minimizes defects
- Optimizes temp. for specific properties
- Enhance quality progressively with ML
- Engineers trained in AI/ML for diagnostics
- Every correct prediction saves product
- Reduces waste
- Improves yield and reduces costs
- Up to 50% of discards avoided
- $8M-$12M conservative savings estimate
- ~$1M Month
- Maintain extremely high quality standards
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Learn how the project partners identify huge potential for the application of machine learning to predict product defects early in the production line.
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