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Session

Case Study

Monday, September 29

06:00 PM - 06:30 PM

Live in Berlin

Less Details

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?

Presentation

Speaker

Marcel Wabo

CIO, Business Area User Experience, Continental Automotive Technologies GmbH

Excited to lead and transform our strong Automotive IT organization in the APAC region. Senior IT Director offering 21+ years of successful implementation of strategic, tactical, and operational business initiatives within the Automotive industry. Proven expertise in leading large-scale projects and
organizational changes. Result oriented leader with a strong focus on innovation, standardization, and
efficiency. Manages strategic relationships with internal and external stakeholders to establish long-
term trust and partnership. Purposeful leader, inspiring others to their best self and developing a high performing culture by means of clear vision, impactful communication, and individual coaching.
Turning diversity and global challenges into opportunities. Fluent in 3 languages (English, German,
French).

Company

Continental Automotive Technologies GmbH

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