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Modern Design of Experiments (DOE)

Modern Design of Experiments

Cy Wegman, Course Author

Senior Data Analyst and Statistical Consultant at Predictum Inc.

2 days (delivered as 4 half-day sessions)
On-premises or virtual
Available on request

About This Course

Design of Experiments (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 the most up-to-date and effective DOE skills needed to optimize your products and processes. This includes use of custom designs, space-filling designs, and SVEM machine learning. We also delve deep into foundational skills you need, like 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.

  • Review good experimental planning practices
  • Construct experimental designs using JMP
  • Evaluate and compare the effectiveness of experimental designs
  • List advantages of designed experiments over the one at a time method
  • Formulate predictive mathematical models from designed experiment data
  • Judge the predictive capability of the models
  • Develop sampling plans for experiments that mitigate signal to noise issues
  • Identify feasible and optimal factor combinations of the experimental region
Applications pertain to all industrial sciences. Examples of industries in which we have had success include:
  • Batteries – improving battery life and exploring new materials
  • Consumer Products, developing superior products at lower cost, and weighing costs of factors and resulting product quality
  • Solar, improving efficiency and cost of solar cells
  • Metals manufacturing, optimizing critical characteristics in metallurgy
  • Pharmaceutical and bio-engineering, conducting media or buffer optimization experiments for increased protein yields from bacteria or mammalian cells
  • Semi-conductors, modeling yield on wafers, identifying yield loss mechanics, and investigating new wafer substrates

Session One

  • Introduction
  • Navigating JMP
  • Introduction Workshop
  • Improvement Through Modeling
  • Experimental Planning

Session Two

  • Concepts in Experimental Design
  • Regression Statistics Review
  • Sampling Strategies
  • Multiple Linear Regression

Session  Three

  • Exploration and Optimization
  • Custom Design
  • Space Filling Design & SVEM

Session Four

  • Augment Design
  • Final Project Exercise and Review


Cy Wegman
Sr. Analyst

Cy Wegman

Specialist in data analytics, empirical modeling, and optimization.

Cy Wegman is a Senior Analyst at Predictum. He 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.

Who Should Attend

Engineers, Scientists and Technicians responsible for product and process development.


2 days  (delivered as 4 half-day sessions)

Delivery Methods

On premises or virtual


Familiarity with computers and spreadsheet software, such as Microsoft Excel.

JMP is required if you want to actively participate in the course. If you do not have JMP you can get a free trial version to install on your work or personal computer.


Individual and group pricing available on request

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Modern Design of Experiments

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