UPDATE | June 24, 2026
From process monitoring to manufacturing intelligence: Real-time analytics and predictive control in biomanufacturing
Modern bioprocesses generate vast quantities of data, but deriving predictive and operationally meaningful insights remains a key challenge.
This whitepaper explores how Samsung Biologics is advancing its digital bioprocess framework by integrating multivariate data analysis (MVDA), predictive modeling, and explainable AI to enhance process understanding and manufacturing insight.
Key highlights include:
MVDA-based monitoring of batch trajectories and process deviations
Predictive modeling to support earlier insights into titer, viability, harvest readiness, and process risk
SHAP-based explainable AI for interpreting model predictions and variable-level contributions
Secure client-facing monitoring capabilities designed to support transparent process oversight and technical collaboration
A long-term digital twin strategy integrating data-driven and mechanistic models
Discover how integrated monitoring, predictive analytics, and explainable AI can support more informed and proactive bioprocess decision-making.
Modern bioprocesses generate vast quantities of data, but deriving predictive and operationally meaningful insights remains a key challenge.
This whitepaper explores how Samsung Biologics is advancing its digital bioprocess framework by integrating multivariate data analysis (MVDA), predictive modeling, and explainable AI to enhance process understanding and manufacturing insight.
Key highlights include:
- MVDA-based monitoring of batch trajectories and process deviations
- Predictive modeling to support earlier insights into titer, viability, harvest readiness, and process risk
- SHAP-based explainable AI for interpreting model predictions and variable-level contributions
- Secure client-facing monitoring capabilities designed to support transparent process oversight and technical collaboration
- A long-term digital twin strategy integrating data-driven and mechanistic models
Discover how integrated monitoring, predictive analytics, and explainable AI can support more informed and proactive bioprocess decision-making.
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