Consumer products need to be reformulated continually in order to adapt to new market forces, government regulations, and supply chain limitations. In some markets, sustainability initiatives and consumer demand for natural ingredients are forcing major reformulation efforts across entire product lines. These sweeping changes are creating an R&D and innovation bottleneck. Companies that are able to innovate and reformulate faster, removing the bottleneck, stand to gain significantly increased brand awareness and market share.
Accelerated product formulation optimization is one of the major use cases for machine learning and AI, but understanding where and how to successfully use these tools to reach business objectives can be challenging. In this seminar, we’ll discuss this problem from an industry R&D perspective and demonstrate how to leverage historical data and modern digital technologies, including machine learning, to develop new and improved products in the lab much faster than using traditional experimental methods.
Key Learning Objectives:
- Learn how machine learning and AI can enable product reformulation with less trial-and-error
- Understand why appropriate scientific data management is needed for data-driven modeling
- Discover why R&D teams need in-house machine learning and programming skills to tackle hard problems with less data
Who Should Attend:
- R&D digital transformation leaders
- Product innovation leaders
- R&D managers
- R&D scientists and engineers
- Laboratory technicians
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