DOE and Predictive Tools

How R&D teams use their existing experiments to choose the next formulations to test, with every suggestion showing the constraints, targets and model behind it.

How R&D teams make ingredient properties part of the model itself, so predictions rest on chemistry instead of ingredient names, and a new supplier grade doesn't restart the learning curve.

Whey protein concentrate now costs more than twice what it did a year ago. Brands are blending in MPC, soy and pea, but no single protein does everything whey does. Here's how to design the blend instead of guessing it...

AI-guided experiment design can cut the number of runs a project needs. The DMTA loop only gets shorter when results reach the next design decision faster, and that depends on structured data and scientists who trust the model...

Explore Data now supports 3D scatter charts, so R&D and QC teams can plot three properties at once, color by a fourth, and rotate the view to catch groupings, outliers, and spec compliance that a flat chart can hide.

Analytics and experimentation are both called AI, but they do different jobs: one explains your past results, the other proposes your next experiment. This piece breaks down the distinction and why both depend on structured R&D data first.

Uncountable's ML team's talk at Beiersdorf's AI Month: how Bayesian optimization and Gaussian processes let the data pick the next experiment, hitting R&D targets in fewer trials. Structure first, AI second, augmenting scientists not replacing them.

AI reliably speeds up early-stage R&D discovery and optimization, but hasn't yet reduced late-stage clinical attrition. This piece breaks down where AI's gains are real, where they're overstated, and why structured data determines whether it works.

A free browser game that teaches design of experiments. Find the best sourdough loaf in 8 bakes and see why changing one thing at a time fails.