Andreas Kunov-Kruse

This interactive workshop moves beyond theory to explore how real‑time process monitoring and predictive control are being practically applied across pharmaceutical and biotech development and CMO/CDMOs. Through real‑world case studies and facilitated group discussions, industry experts will demonstrate how sensors, PAT, and advanced analytics are being used to surface actionable insight earlier; when it matters most.

Learn how to detect deviations earlier, improve decision‑making, and reduce batch failure risk, while overcoming the organizational, technical, and operational barriers to adoption. Designed to be highly participatory, this workshop will equip attendees with concrete learnings they can take back to their development labs and manufacturing sites, thus helping turn real‑time data into predictive, process‑ready control strategies.

Industry experts will cover:

• Applying machine learning models and digitized process development data, to identify edge case scenarios and predict potential impacts on critical quality attributes in real time
• Using models as decision support tools to enhance confidence in PARs, improve anomaly detection, and support batch disposition while maintaining established quality and investigation processes
• Addressing key challenges in validating ML based approaches, establishing governance frameworks, and demonstrating commercial value
• Determining when process monitoring and PAT integration meaningfully enhances process understanding, supports robust control strategies, and justifies investment
• Implementing data collection and monitoring tools to provide a holistic view of aseptic processes, enabling stronger decision making, improved control, and long-term manufacturing performance