Digital Twin Technology for Biologics: Predictable Scale-Up and Quality Across the Value Chain
Digital twins are emerging as practical decision-support systems for improving predictability across biologics development and manufacturing. They enable organisations to combine process data, equipment information, quality attributes and operational insights to simulate, monitor and optimise complex biological processes. This half-day program introduces digital twins in the context of biologics process development, scale-up, manufacturing operations and quality management. Participants explore how digital twins differ from conventional dashboards, simulations and Process Analytical Technology (PAT) systems. The program covers value-chain applications including clone/process development, scale-up, technology transfer, manufacturing and quality. The session also addresses critical implementation considerations including data architecture, model confidence, validation, regulatory expectations, governance models and enterprise scaling.
Who Should Attend
- Biologics R&D professionals
- Process Development teams
- MSAT professionals
- Manufacturing and Operations teams
- Quality Assurance professionals
- Engineering and Digital Transformation teams
- Data and Analytics professionals supporting manufacturing
- Strategy and Innovation teams evaluating digital transformation opportunities
Business Outcomes
- Identify where digital twins can create measurable value in biologics operations.
- Understand the data and governance foundations required for implementation.
- Prioritise digital twin use cases based on business impact and feasibility.
- Connect digital twin outputs with quality, yield and cycle-time improvement decisions.
- Build a structured roadmap for pilot implementation and enterprise adoption.
- Improve collaboration between process owners, digital teams, QA and manufacturing stakeholders.
Course Content
Meet Your Instructor
This course includes:
- Lessons 3
- Topics 90
- Duration 4 Hours
- Quizzes 0
- Language English