White Paper
Reducing experimental time and cost by 50-80%
Identifying the optimal composition or chemistry and the ideal processing parameters to achieve commercial performance goals as quickly as possible is the key objective in the design of formulations, chemicals, materials, and biopharmaceuticals. Traditional approaches suffer from key disadvantages: expert-driven design is labour-intensive and time-consuming; single-factor analysis misses the effects of correlations between factors; and conventional Design of Experiments is exhaustive but focused on covering the design space rather than rapidly achieving performance goals.
Machine learning identifies improved products and processes much faster than traditional approaches, by focusing experimental effort directly on those routes most likely to be successful. With the experimental costs associated with a typical R&D project in industry running to hundreds of thousands of dollars, a 50-80% reduction in the number of experiments required can deliver a very significant return on investment
