They wanted to integrate all their existing data, despite variations in formulation types—whether measured in parts per hundred resin (PHR) or weight percentage (wt%). Additionally, they sought to determine the best approach to effectively incorporate and impart knowledge of the NCO (Isocyanate) index into their predictive models. Another critical objective was to perform predictive analysis on a less-known ingredient by leveraging data points from a well-known ingredient, enabling them to expand their research capabilities and innovate with new materials. The ultimate goal was to develop a robust and flexible modeling framework that could handle these complexities, ensuring accurate predictions and optimized formulations for their diverse production processes.