One of the great things about humans is that we are all unique. Diversity has benefits all around us, but variation in preferences with regard to consumer goods makes it challenging to predict what will delight the greatest number of people. The use of design of experiments (DOE), facilitated by JMP, is critical to designing formulations with the most appeal. 

The objective of this presentation is to interactively present methods to optimize formulations for the greatest consumer liking. Two DOE approaches from a real food formulation case study will be presented: an 18-run definitive screening design (DSD) and an 18-run space-filling design modeled using SVEM and neural networks.

To prepare data for modeling, multidimensional scaling is demonstrated to remove anomalous participants’ data. Participant clusters built using hierarchical clustering are used when fitting models using fit definitive screening and SVEM neural networks. The power of the JMP profiler will highlight how consumer preferences differ by cluster. Text Explorer is used to show how to verify insights gained through modeling by exploring verbatim comments by study participants. Lastly, insights gained from each experimental design and modeling approach are compared along with limitations of each. Attendees are presented with a more information-rich alternative to traditional DOE and consumer testing strategies.