Laura Lancaster, Principal Research Statistician Developer, SAS
The prediction profiler in JMP® is a powerful tool for visualizing and optimizing models from designed experiments. This presentation will focus on new features in the prediction profiler for exploring and optimizing models with known constraints and for determining factor ranges that assure quality as defined by the specifications associated with Critical Quality Attributes (CQA), thereby solving a fundamental Quality by Design (QbD) problem. While previous versions of JMP were able to create designs that respected disallowed combination constraints and combinations of factors that are known in advance to be physically impossible or undesirable, the model exploration and optimization in the profiler in the last step were unable to obey these constraints. We will demonstrate how the profiler, since JMP® 16, handles these complex design constraints automatically when exploring the model and performing optimization. We will also demonstrate how the Design Space Profiler, new in JMP® 17, finds subregions of the design space that maximize the probability of maintaining product quality for the CQA specifications while maintaining maximum flexibility. These two capabilities make the prediction profiler indispensable for a high-quality product and process innovation.
