Biopharmaceutical manufacturing’s competitive advantage is shifting from biological advances to cost effectiveness and resilience. Succeeding isn’t as straightforward as merely improving process efficiency, though. Key challenges include process uncertainty and batch-to-batch variability, manufacturing dynamics, and addressing market pressure to meet growing demands at lower costs.
Transitioning “from science labs to smart operations...
requires a proactive use of data analytics and operations management to inform daily decisions,” according to a recent by scientists from Eindhoven University of Technology (TU/e), MSD, Merck USA, GEA Group, and Ceva Santé Animale. The roadmap they developed begins with strategic vision and the right technology infrastructure, such as continuous manufacturing and real-time release testing. The process intensification inherent in continuous manufacturing makes it attractive to biomanufacturers.
Now, more sophisticated sensors are providing processing data that otherwise is difficult to obtain. Consequently, “Continuous manufacturing systems will generate large amounts of process data,” Tugce Martagan, PhD., associate professor, TU/e, told .
To achieve greater gains, however, “We also need to equip these new technologies with operations management/AI-driven control algorithms to achieve optimal performance,” Martagan says. By combining in-depth sensor data with increasingly sophisticated analytics and operations management strat.