Using Design of Experiments (DOE) to Optimize and Innovate

This web seminar dives into the contrast between testing variables “one at a time” vs. “testing several” through Design of Experiments (DOE). Tom Donnelly and Jed Campbell, subject matter experts at JMP, swiftly navigate through the critical need for DOE (a branch of applied statistics) and introduce key tools in this process. Through interactive demonstrations and captivating real-life success stories, they will convey the message that if you're not already using DOE, you may want to start. Their analysis and testimony will portray a strong correlation between accelerated innovation and tailored experiments that meet your specific needs.

Key Learnings:

• Understand the importance of Design of Experiments (DOE) for rapid process optimization.
• Discover the power of JMP's Prediction Profiler for intuitively understanding your model, and for communicating results to decision makers.
• Learn to address common hesitations and doubts about adopting DOE.
• Explore the broader applications of DOE beyond process variables.
• Use DOE methods to reduce the cost of high-performance computer simulations, or to make combinatorial software testing more robust and efficient.

Tom Donnelly is a Principal Systems Engineer at JMP, where he supports users in the defense and aerospace sectors. He has been actively using and teaching design of experiments (DOE) methods for the past 35 years to speed development and optimization of products, processes and technologies. Prior to joining JMP, Tom worked as an analyst for the Modeling, Simulation & Analysis Branch of the U.S. Army’s Edgewood Chemical Biological Center. For 20 years, Tom served as a partner with the first DOE software company to enter the market, teaching more than 300 industrial short courses to engineers and scientists.

Jed Campbell is a Systems Engineer at JMP Statistical Discovery, which creates interactive and highly visual statistical discovery software for scientists and engineers. An educator by training, Jed helps people see how they can get better and faster insights to their data by using JMP.

A Master Black Belt in Six Sigma, he has a vast experience as a problem solver, ranging from process improvement in manufacturing to designing and coaching experiments in rocket science. Jed led a successful application process for which he won the enterprise-level Shingo Prize in 2011. He has a strong business acumen, sharpened by a nationally ranked MBA, focusing on entrepreneurship. Jed also understands quality systems and how they can be incorporated into existing businesses without undue bureaucracy.