Continental has successfully implemented a predictive quality system powered by AI and analytics to detect production anomalies before they lead to defects. By integrating machine learning into its manufacturing process, the company has improved product quality, reduced waste, and enhanced operational efficiency. This session explores how Continental brought predictive quality from concept to reality.
- How did Continental shift from reactive quality checks to predictive insights?
- What types of data were used to train and refine AI models for real-time quality forecasting?
- Which measurable improvements in quality and efficiency were achieved after implementation?
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