Deploying DOE to Predict and Improve Process Performance

Design of experiments (DOE) is a tried-and-true, multifactor quality tool for identifying key process drivers. This presentation demonstrates how to deploy DOE to create reliable prediction models. Case studies from a variety of industries demonstrate how to construct these models and then use them for determining more desirable process settings.

Although DOE tools require a high level of stats and math, modern-day software bears the burden of the computations. Therefore, the focus of this talk will be kept to the need-to-know elements for successful experiment design, analysis, modeling and optimization. Ultimately, effective application of DOE produces very useful predictive models that lead to profound process understanding. These models enable manufacturers to build higher-quality products at lower overall cost.

This talk presents key strategies to build DOEs that are aimed at prediction—not simply to detect effects. It lays out response surface methods (RSM) geared for most accurate and precise model generation. Participants will be briefed on the key statistics that quantify the ability of the model to make good predictions, as well as tools for final optimization. The presentation concludes with helpful tips on choosing the right DOE for the problem at hand.

Shari Kraber Shari is a consultant with Stat-Ease, with over 20 years of experience teaching engineers and technical professionals how to design and implement design of experiments. Shari draws on her process engineering experiences at 3M and Frigidaire where she implemented statistical quality tools such as statistical process control and DOE. As the Stat-Ease Workshop Manager, she now devotes her considerable energy, experience and leadership skills to workshops on DOE, teaching others how to make breakthroughs. Shari is also the Marketing Manager for the International Statistical Engineering Association, a non-profit organization focused on teaching people how to solve complex problems (

Shari graduated with a BS in Industrial Engineering from Iowa State University, and a Master of Science in Applied Statistics from Rochester Institute of Technology. She holds her professional engineering license in the state of MN.