From Process Monitoring To Manufacturing Intelligence: Real-Time Analytics And Predictive Control In Biomanufacturing

By Jihye Heo, Senior Engineer, and Jinhyoung Lee, Lead Engineer, Digital Twin, Samsung Biologics

As biomanufacturing processes become increasingly data-rich, the challenge is no longer collecting information but transforming it into actionable insights that improve process understanding and operational performance. Emerging digital strategies are addressing this challenge by combining multivariate data analysis (MVDA), process analytical technologies such as Raman spectroscopy, predictive modeling, explainable AI, and digital twin frameworks into integrated monitoring and decision-support systems.

These approaches enable manufacturers to move beyond retrospective analysis and real-time monitoring toward predictive process management. By leveraging historical and live process data, predictive models can forecast key outcomes such as titer, viability, harvest readiness, and potential process risks. Explainable AI techniques, particularly SHAP-based interpretation, provide transparency into model outputs by identifying the process variables that most influence predictions, helping scientists and engineers connect data-driven insights to underlying biological and operational behavior.

The next evolution of these capabilities involves secure data-sharing environments that improve transparency and collaboration among manufacturing stakeholders, as well as hybrid digital twins that combine mechanistic and data-driven models. When integrated with model predictive control (MPC), these systems can simulate future process states, evaluate alternative operating strategies, and recommend optimal actions within validated process boundaries. Together, these technologies represent a practical pathway toward more predictive, transparent, and intelligent biomanufacturing operations while maintaining appropriate human oversight and regulatory compliance.