Within this thesis we implemented online process monitoring and control in our labs using PAT (Process Analytical Technology) tools and scale-free control variables in mammalian CHO (Chinese Hamster Ovary) cell culture processes to improve productivity, robustness and predictability at different scales. Predictive models for cell physiology and sensor signals were established based on historical data analysis of industrial process development data at lab (2L) and pilot plant scale (80L). The developed models proved to be scale-independent and transferable to other CHO clones, which allowed their application on different processes with limited prior information. Optimization of feeding in fed-batch mode was achieved by controlling the specific glucose consumption rate within a narrow range in real time using PAT tools, such as an online metabolic analyser and a capacitance probe for monitoring and control purposes. This led to very stable glucose, lactic acid and pH profiles, improving productivity and robustness of the platform process with scale-free parameters. Mechanistic, statistical and in-silico models under dynamic fed-batch conditions were used to gain novel insights into cell metabolism, and allowed a predictive run forecast at 2L and 12000L scale. The established methodologies facilitate and improve process transfer and scale-up of industrial mAb (monoclonal Antibody) platform processes through advanced process monitoring and control. This is in line with recommendations from the FDA (Food and Drugs Administration) and EMA (European Medicines Agency) to implement PAT & QbD (Quality by Design) approaches in the biopharmaceutical industry to ensure consistent quality of medicines for the safety of patients.