Phil Kay, Learning Manager at JMP, is a passionate advocate for the use of Design of Experiments (DOE). From his own experience as a chemist, he found that once started using DOE in his work, experimenting with multiple factors at a time instead of one factor at a time was eye-opening and transformative.
Phil was recently interviewed by Dr. Fane Mensah for the life science podcast CABTalks by Synthace. They spoke about the advantages of DOE, the need of teaching multifactored experimentation at universities, the fear of automation and the future of science.
The main point of DOE, says Phil, is to “most efficiently and most effectively learn from your experiments.” He points out three big impacts that the use of DOE can have:
- At the company level: Getting the products to market faster. In the development of Covid vaccines “design of experiments was key to developing those and getting them out to the world.”
- At the R&D level the “big benefit is predictability and productivity.”
- At an individual level:” I just think it makes you a better scientist, right? It makes you more employable. It gives you successes that can boost your career.”
Even though science is becoming more and more digital and reliant on data, Phil does not believe that scientists need to learn how to code. “I do think we need scientists to have a good awareness around data. I think they should be capable of visualizing data, exploring data, using some, some of the, you know, at least some of the simple kind of data analytics tools.”
Listen to the podcast for more great insights on DOE and the future of science and automation.
You can follow Phil’s weekly thoughts and tips on DOE by following the hashtag #DOEbyPhilKay on LinkedIn.
