Optimizing manufacturing performance has been at the top of the pharma industry’s priorities—especially in the context of complex drug substance production, such as antibody-based modalities. The pressure on technical operations executives to improve on productivity, efficiency, speed and process robustness is stronger year-on-year, and challenges persist in predicting and controlling key parameters to optimize bioreactors through titer, yield or critical quality attributes.
Roche has tackled that problem head on by harnessing the power of artificial intelligence to predict and support real-time shop-floor decision-making to optimize key manufacturing parameters. These advancements are being industrialized across the network to enhance productivity and robustness. In this webinar, ZS’s Romain Bonnot and Roche’s Hadj Latreche discuss Roche's vision, approach and challenges, as well as its current state of deployment.
You will learn:
- How AI models support manufacturing decision-making today
- The journey to AI at scale
- Roadblocks and key success factors, including how to operationalize new ways of working
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