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Design of Experiments (DOE)

Design of Experiments (DOE)

About this Course

Design of Experiment (DOE) is the most effective way to do research. The reason is simple: DOE enables users to uncover deeper insights in less time and cost. 

In this course, you will master the fundamental DOE skills needed to optimize your products and processes. This includes applying DOE using JMP, designing efficient experiments, and building predictive models.

Upon completion, you will be able to advance your organization’s research and improve productivity considerably over informal methods. 

What you will learn

  • Mixture experiment terminology and applications
  • Shortcomings of classical mixture designs and approaches
  • Self-Validating Ensemble Modeling (SVEM) methodology for analyzing mixture experiments
  • Use of space-filling designs for a wide range of mixture situations
  • Optimization of the response surface over the design space
  • Optimization for multiple responses
  • Mixture and process factor experiments
  • Mixture of mixture experiments

Who Should Attend

Engineers, Scientists and Technicians responsible for product and process development

2 days

Familiarity with computers and spreadsheet software, such as Microsoft Excel

Individual and corporate pricing available on request


Cy Wegman
Sr. Analyst

Cy Wegman

Specialist in data analytics, empirical modeling and optimization

BSCE: Rose Hulman Institute of Technology • 38 years Procter & Gamble
• Paper Manufacturing, Paper Engineering, Global Engineering • Corporate Leader for Empirical Modeling & Optimization • P&G Prism Society • 7 years Predictum

Cy Wegman specializes in data analytics and empirical modeling and optimization. Before joining Predictum, he worked for 38 years at Procter & Gamble, where his last assignment was the Empirical Modeling leader for the company.

Cy is a member of the P&G Prism Society, which represents the top 1% of P&G engineers, for profitably applying his technical mastery. His contributions have resulted in hundreds of millions of dollars in annual savings. His cross-industry work in manufacturing, engineering, and R&D has spanned molecular-scale to full-scale technologies, as well as material, formulation, process, packaging, and consumer models.

Cy holds a B.S. in Civil Engineering from Rose-Hulman Institute of Technology.

Course Syllabus

Day 1

  • Introductory Material  
  • Modeling Overview  
  • Regression Statistics  
  • Multiple Linear Regression  
  • Design of Experiment Concepts  
  • Full Factorial Designs   

Day 2

  • Augmenting Factorial Design
  • Fractional Factorial Design
  • Plackett-Burman Design
  • Definitive Screening Design 

Day 3

  • Fractional Factorial Designs  
  • Screening Designs  
  • Custom Designs  
  • Space Filling Designs  
  • Final Project 

Knowledge you can put to work

Register for "Mixture Experiments Using Machine Learning: 'A How-to Approach'"