Boehringer Ingelheim: DOE and Bayesian Optimization for Faster, Smarter Experimentation

When multiple inputs interact – making it unclear which ones matter – trial-and-error experimentation slows progress. Natural process variation adds noise, and too many runs fail to provide clear answers. In this webinar, discover how combining design of experiments (DOE) with Bayesian optimization helps you identify what works, faster.

If running a full DOE isn’t feasible given limited time, budget, or resource slimits, Bayesian optimization continues to move your data forward – enabling you to learn from every run and focus future tests on the most promising areas.

Learn how a machine learning-assisted approach can recommend the next best experiment based on your goals – enabling you to achieve better outcomes with fewer trials and less waste.

Key takeaways:

• How DOE and Bayesian optimization work together to accelerate R&D and manufacturing.
• Practical ways to save time, cut costs, and preserve resources while improving outcomes.
• How to decide what to test next with confidence, even in noisy environments.
• Examples from industry leaders showing proven, real-world impact.

What if I’m new to Bayesian optimization?

No prior experience is required. This on-demand webinar provides thorough explanations, peer insights, and short demos that are suitable for any skill level.