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
- 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
- Navigating JMP
- Introduction Workshop
- Improvement Through Modeling
- Experimental Planning
- Concepts in Experimental Design
- Regression Statistics Review
- Sampling Strategies
- Multiple Linear Regression
- Exploration and Optimization
- Custom Design
- Space Filling Design & SVEM
- Augment Design
- Final Project Exercise and Review
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.
Modern Design of Experiments
Please complete the information below. Our team will be in touch with you to schedule your course.
Additional questions? Contact firstname.lastname@example.org.
We look forward to working with you soon!