As we develop and transfer processes into manufacturing, scientists and engineers perform studies iteratively and develop process and product understanding. To help teams or organizations with high staff turnover retain insights, we need to address several challenges. How can we:
Help teams compare and contrast the efficiency and effectiveness of historical experimentation?
Show what assumptions were made and identify knowledge gaps?
Help teams gain additional insight from disconnected tabulated data sets and free text?
Discover what we need to know when it’s not always clear?
I have challenged participants in both industry and academia to study the same process in a series of workshops. Teams select different factors and ranges and perform different classical, custom and definitive designs. I then help participants compare their own output with studies carried out in previous workshops by different teams. We use the holistic learnings to generate new ideas and actionable decisions. The JMP 13 Early Adopter program has given additional insight as to how we can enhance our use of prior information. This presentation will illustrate how new functionality in JMP 13 (e.g., Query Builder and Text Explorer) can help us build a more accessible and structured “organizational memory” of both observational and experimentally derived data.