The classical “Statistical DoE” applies experimental screening- and optimization designs templates for the screening, optimization and robustness testing of processes. This type of DoE is used in case no or little prior process knowledge is available. However, since for most industrial processes a high degree of knowledge is available, statistical DoE is typically not the most efficient strategy. Technologies are needed to efficiently incorporate the available knowledge on the process to increase the experimentation efficiency. Mechanistic DoE, or M-DoE, incorporates engineering knowledge in the form of constraints, expert-rules and known mechanistic relationships to efficiently experiments to identify optimal process conditions faster. Thereby, process optima are identified using less experiments and DoE design pitfalls are avoided.