Antibody purification processes are typically developed using a platform approach. Optimal operating conditions for single units are found by altering just a few process parameters with Design-of-Experiment (DoE) procedures or high-throughput screenings. But what if DoE fails to find robust process conditions? This article shows how to achieve higher yields using mechanistic models to optimize an antibody purification process.