Digital and data-rich methods are becoming omnipresent in process development workflows. During preliminary optimization efforts, synthetic route scouting is often augmented by computer-aided synthesis planning (CASP) tools, including machine learning (ML) algorithms. Reaction optimization within the chosen route can be performed using a myriad of data-driven approaches, including closed-loop optimization. Further detailed reaction optimization and modeling then provides additional understanding toward a robust procedure. Finally, by combining reaction models with reactor models, the entire process stream can be simulated, resulting in a digital twin that can be used to track the state of the product at any point in space and time.