Bayesian optimization seems to be the rising star on the stage of industrial experimentation, but how does it fit alongside traditional DOE approaches? Its adaptive, goal-oriented strategy can dramatically improve efficiency and insight generation in complex systems, yet DOE remains a cornerstone in industry.

In this talk, I share learnings from my research and discussions with practitioners, outlining practical strategies for applying Bayesian optimization within JMP workflows and bridging classical and modern methods for industrial experimentation.