We have worked with formulation scientists and engineers for decades and have seen many different types of formulation development programs. This has shown us what formulation scientists really need to know rather than what is nice to know. Because JMP software is used in the examples in the book, readers get valuable guidance on the software for the proposed methodology. That means JMP users can immediately apply what they learn in the book.
Key takeaways from the book include:
Approach the development process from a strategic viewpoint, with the overall end in mind. Don’t necessarily run the largest design possible. An experimentation plan that implements the strategy provides the right road map for developing a successful formulation.
Focus on developing understanding how the components blend together. Use designs and models that help find the dominant components, components with large effects, and components with small effects.
Use screening experiments early on to identify those components that are most important to the performance of the formulation. This strategy creates a broad view and helps ensure that no important components are overlooked. It also saves significant experimental effort.
Analyze both screening and optimization experiments using graphical and numerical methods, which is easily done with JMP. The right graphics can extract additional information from the data.
Consider integration of both formulation components and process variables in designs and models, using recently published methods that reduce the required experimentation by up to 50 percent.
This is how you speed up the formulation development process and produce high-quality formulations in a timely manner. Upcoming blog posts will show how to address each of these important issues.