JUNE - 20238MANUFACTURING TECHNOLOGY INSIGHTSIN MY OPINIONIn the last decade, the topics of big data, machine learning (ML) and artificial intelligence (AI) have gone from being the domain of technical specialists to a strategic imperative across all dimensions of most business enterprises. The chemical and materials industries are transforming the way materials innovation is done ­ from early discovery to product development, through scale-up into manufacturing, to customer engagement and support. The need of the hour is to harness the power of empirical data and fundamental modeling to predict product performance properties and innovate a new and more efficient era for manufacturing. A REALIZED EXAMPLE: PREDICTIVE INTELLIGENCEMy company, Dow, is celebrating its 125th anniversary this year. 125 years of existence equals 125 years of data. With the advent of AI and ML, we've put this big data to use in the creation of numerous predictive capabilities from Paint Vision in the Coating business to the Predictive Intelligence (PI) capability in our Polyurethanes business.These capabilities place our vast material science expertise and immense data archive at the fingertips of our customers. Diving into the PI capability as an example, it completely transforms product formulation process by predicting formulation properties and simulating customer processes. For a single new formulation, there might be over 15 variables with 1000 different options each, leading to over a million data points to consider. The R&D process in the lab for a new formulation would take scientists months just in the discovery phase, but using the power of digitalization, our predictive modeling capability can take over for this discovery phase and drastically cut down the time to market by many months. THE REALIZATION JOURNEYThe vision and promise of predictive modeling is easy to embrace when you see the immense benefits of capabilities like PI in action. The actual journey that so many companies are on is complex and fraught with pitfalls and frustrations. As a passionate advocate for the digital transformation of innovation, I have personally been on a journey with several teams at Dow and we have learned lessons along the way which I would like to share for those interested in joining the digital revolution. The starting orientation of technical professionals (whether R&D or IT or manufacturing) is usually to start with the domain expertise aspects of the problem. But this approach can be shortsighted, putting the cart before the horse. I believe the problem needs to be approached holistically across multiple inter-related elements. HOW CAN YOUR COMPANY EMBRACE THE VISION AND PROMISE OF PREDICTIVE MODELING TO USHER IN THE FUTURE OF MANUFACTURING?By Sarah T. Eckersley, VP R&D, Industrial Intermediates & Infrastructure, DowSarah T. Eckersley
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