• Is the current infrastructure equipped to support the modern production methods of smart factories, and if not, what are the primary challenges hindering its adaptation?
• What potential solutions can be implemented to meet the requirements of modern production approaches and overcome the obstacles in adapting the infrastructure? How can we maintain flexibility in manufacturing processes to accommodate changing demands?
• How should concepts for integrating and connecting diverse systems and processes within smart factories be approached? What strategies can be employed to ensure smooth integration and connectivity while optimizing operational efficiency?

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?