Tannaz Tajosleiman, Data Scientist, nne
During a process development phase, the process operating conditions must be characterized and optimized at upstream and downstream levels. However, despite the well-known effect of the upstream outcome properties on the performance of the downstream, these two levels are often studied and tested separately, and the interlinked correlations are ignored. As a result, it’s not uncommon to see cases where a significant improvement in the upstream is not retained downstream and, even in some extreme cases, causes additional downstream challenges and a lower overall process yield. Hence, it is important to have a comprehensive cross-step view in the design of the experiments during the process characterization. In this regard, the JMP DOE toolbox is ideal for addressing such cases. In this work, a case study will be presented that demonstrates a systematic two-step method to use the JMP custom design DoE toolbox and the covariate factors option for a cross-step experimental design. The design focuses on pairing the upstream and downstream experimental conditions in an efficient manner. Moreover, operational practicalities such as batch parallelization are included in the final design.
