![]() ![]() Over the summer I had an intern – a computer science PhD candidate – and I got a glimpse at what at least that one computer scientist brought to the game. My guess is that it will be a marriage of statistics and computer science. So while it may not happen immediately, it means I've got time to carefully consider the types of qualifications that I want for the position. How did that come about?ĭan: To start with, we don’t plan to add headcount without seeing a reduction elsewhere – we’ll use attrition to enable a new position. Meg: I understand you’ve gotten the green light to hire someone in a new role providing statistical support in R&D. You always want to have pull from your customers – who, in my case, are chemists – but sometimes the executives push too. The more that our executives saw the types of improvements that we were making relative to our products, the more they insisted upon being used in new projects. Meg: How have statistical approaches like DOE been received?ĭan: When introducing a new concept, you're always going to have some people who are ready to jump on board right away and others who are reluctant – it’s human nature. And the more we’ve learned, the more we’ve accelerated our development process in a way that has brought about some fairly substantial advances in our products. So yes, the fact that PhD chemists take my advice on chemical formulations is pretty interesting. That said, to date, my recommendations have never once been rejected by a chemist. ![]() We have found that when utilizing the software, the predictions are sometimes outside industry norms and assumptions, which has been surprising to me and the chemists…and they are yielding great results in the lab and in field trials. I think you need a reasonable theoretical background to convince chemists (or even other scientists) that you can help them to be even more successful by narrowing the window of experimentation, reducing bench time and accelerating R&D. While I don't do formulations myself per se, I do make recommendations based on what the software lets me know about the way fit together. I'm working to change, and not only does statistics add significantly to their knowledge and result in better understanding of the design space, but it also reduces the total number of experiments they have to run. When you've got a high-dimensional design space, you need to help determine whether you've sufficiently explored it.Īnd that’s what I help with: I help chemists gain new knowledge – and I provide support when they’re exploring the design space. And when you throw in nonlinearity, there’s just no way to do it effectively without statistical tools! Sure, it might be easy for chemists to experiment in incremental amounts,, but it's not an efficient way of doing experiments. In general, the human brain has a difficult time understanding and applying the concept of complex chemical interactions. Meg: I understand you’re the only person without a chemistry background in Hexion’s R&D group! How has that influenced your perspective on the ideal balance between chemistry and statistical expertise in industry?ĭan: At Hexion, we are evolving in the right direction as a company: increasing statistical contributions to R&D without reducing the theoretical chemistry contribution. I spoke with Dan during JMP Discovery Summit at SAS Headquarters in North Carolina. He holds a Master’s in Quality, Reliability and Statistical Engineering from Arizona State University. A Lean Six Sigma Master Black Belt, Dan joined the company five years ago to provide statistical modeling and analytical support to R&D activities in resins for engineered wood and slow-release nitrogen fertilizers.ĭan previously served in a range of leadership roles in business excellence and quality engineering for TechnipFMC, Mitsubishi Caterpillar, Maytag and Texas Instruments. Dan Fortune is Manager of Global Growth Analytics at Hexion Inc., a specialty chemical company driving innovation in thermoset resins.
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