Biotech manufacturing is constantly influenced by variations in raw material qualities, biological performance and process changes. The ability to efficiently track and understanding the influence of these changes on the resulting process performance is essential for maintaining quality and optimizing yields. Multivariate (MV) batch analysis methods are powerful tools for characterizing process performance and linking the connection between raw material quality, the manufacturing environment and the resulting product quality. Often important metrics such as biological activity and product yield can be inferred from existing process measurements. A case study is presented demonstrating the ability of these methods to be trained to recognize the need for increased nutrient addition to assure final yields. The MV models are successfully integrated into a closed loop nutrient addition control system resulting in decreased operational costs and increase in yields.