Using Design of Experiments (DOE) to Optimize and Innovate

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.

Your Key Learnings:

1. Understand the importance of Design of Experiments (DOE) for rapid process optimization.

2. Discover the power of JMP's Prediction Profiler for intuitively understanding your model, and for communicating results to decision-makers.

3. Learn to address common hesitations and doubts about adopting DOE.

4. 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.