How virtual models plus operator interaction drive operational excellence
In the era of advanced biologics manufacturing, the convergence of digital modelling, live data and human judgement is transforming how facilities operate. At the heart of this change is the concept of the digital twin — a virtual counterpart of a real process, asset or system that mirrors behavior, anticipates changes, and supports decision-making. When paired with effective human-machine collaboration, digital twins enable biopharmaceutical organizations to shift from reactive manufacturing to proactive, resilient operations.
What is a digital twin (and why it matters in biopharma)
A digital twin is more than a simulation or static model. It is a live, connected virtual replica of a physical process or asset that receives real-time data (or near-real-time), run analytics/simulation, and can support decision-making.
In the context of biologics manufacturing, where living systems (cells, viruses, biomolecules) add variability, and process complexity is high, digital twins offer significant value:
- They enable predictive monitoring of critical process parameters (CPPs) and critical quality attributes (CQAs).
- They support “what-if” scenario simulation (e.g., if feed rate drops, if temperature drift occurs) to test outcomes without risking actual batches.
- They accelerate scale-up, process validation and transfer by providing virtual test beds.
But technology alone is not enough. The human factor — the operator, engineer, QA/QC specialist — remains central to operational success. Without effective collaboration between the human and the machine (model), the promise of digital twins may fall short.
Human-Machine Collaboration: The "Missing Link"
Recent research in biopharma manufacturing highlights a gap: many digital twin initiatives focus heavily on the technology models, automation, analytics — but less on the interaction with the human operator. One review calls for a “collaborative intelligence framework” that integrates operators into digital twin workflows and strengthens trust, usability and decision-making support.
What does effective human-machine collaboration look like in practice? Key elements include:
- User-centric interface design: dashboards, alerts, and visuals must be aligned with operator workflows — enabling quick comprehension rather than overwhelming the user.
- Shared situational awareness: the operator sees the “digital twin view” (predicted trajectory, risk flags) and the live “physical view,” enabling decisions grounded in both virtual and real-world states.
- Decision support not replacement: The twin suggests outcomes or intervention options; the operator validates and executes. This promotes trust and maintains human oversight.
- Training and change management: Operators must be trained to interpret model outputs, understand limitations and collaborate with the twin (rather than treat it as a black box).
- Feedback loops: Operator inputs, outcomes and real-world deviations must feed back into the twin to improve its predictive accuracy over time.
How this drives operational excellence
When well-implemented, digital twins + human collaboration can deliver multiple operational benefits in biopharma:
- Reduced batch failures and deviations: by predicting off-trajectory behavior early and prompting operator action. For example, one site reported early drift detection via hybrid modelling, improving yield and reducing downstream losses.
- Faster tech-transfer and scale-up: twin simulations can test multiple scale/parameter options virtually, enabling operators and engineers to choose optimal conditions with lower risk.
- Improved process understanding and control: The twin provides insight into the interplay of parameters; the human brings contextual judgement (e.g., change-control, process history, regulatory nuance).
- Better regulatory readiness: With digital twin models maintaining traceability of process behavior and human/operator interaction logs, the manufacturing system is more robust to regulatory scrutiny.
- Enhanced productivity and agility: Operators are freed from purely reactive tasks and empowered to make decisions proactively; twin modelling helps simulate future scenarios, enabling the plant to respond more rapidly to change.
Implementation Considerations & Best Practices
For organizations (whether CDMO or internal biologics manufacturer) looking to deploy digital twins with human-machine collaboration, here’s a practical roadmap:
1. Start simple, iterate
- Choose a critical unit-operation (e.g., bioreactor, purification column) with good sensor/data connectivity.
- Build a pilot digital twin model that supports operator decision-making in that scope (rather than entire plant).
- Include operator input from Day 0 to ensure usability and workflow alignment.
2. Establish clean, integrated data architecture
- Sensor/IoT connectivity, MES/SCADA, PAT data streams.
- Unified data model so that the twin and operator interface share a “single source of truth.”
- Ensure data integrity, version control, audit trails.
3. Align digital twin design with human workflows
- Map operator tasks, decisions, hand-offs.
- Design dashboards/alerts with operator in mind (e.g., what does the operator need to know, when, and how).
- Train operators not just on “how” to use the digital twin but on “when” and “why.”
4. Define the human-machine boundary and governance
- Clarify which decisions the twin recommends vs which decisions the human executes.
- Establish escalation and governance protocols (e.g., operator overrides, twin suggestions).
- Integrate twin outputs into change control, deviation investigations, CAPA workflows.
5. Use simulation, scenario planning & continuous learning
- Run “what-if” simulations with the twin (e.g., media feed delay, temperature drift, contamination risk) and involve operators in reviewing outcomes.
- Use real-world operator feedback and outcomes to update the twin model (creating a learning loop).
- Incorporate training drills with the twin-human interface (much like flight simulators train pilots) to build operator familiarity.
6. Scale and expand
- Once validated in one operation, extend the twin + human-machine interface to downstream/upstream steps.
- Monitor KPIs: reduction in deviations, improved yield, faster tech-transfer cycle time, operator satisfaction/trust metrics.
- Establish a governance structure: twin maintenance, model updates, operator training refreshers, continuous improvement.
Real-World Example Snapshot
Samsung Biologics implemented an integrated hybrid modelling approach (combining CFD, DMS modelling, ML/MVDA) which improved operational efficiency and reduced batch failure risk. They upgraded their MES, improved connectivity, and created a unified interface for monitoring and decision support.
In this environment, operators are no longer solely reacting to alarms; they are interacting with a live model that suggests early interventions, and they confirm and execute corrective actions — a true human-machine collaboration.
Challenges and How to Overcome Them
- Data silos & poor connectivity: Many plants have fragmented data systems, which make real-time twin modelling difficult. Overcome by integrating MES/SCADA, standardizing data formats, and investing in connectivity.
- Human trust & adoption: Operators may be skeptical of ‘black-box’ models. Overcome by designing transparent models, explaining twin logic, involving operators in development, training.
- Model validity & maintainability: Biological systems are complex and dynamic — simple models may not capture drift, evolving feedstocks, new modalities. Use hybrid modelling (mechanistic + ML), continuously update models, involve human feedback loops.
- Governance and regulatory compliance: Twin decisions need to be auditable, aligned with QMS, and traceable. Plan from the start for documentation, change-control, operator overrides.
- Change-management and skills: Staff may need new skills (data analytics, twin-interface usage). Invest in training, simulation drills, operator-twin role clarity.
Looking Ahead
As the industry moves toward Industry 4.0 and beyond, the intersection of digital twins, AI, and human collaboration will define next-generation biologics manufacturing. The “digital twin of the organization” (DTO) concept — where workflows, supply chains, manufacturing and quality are all mirrored and optimized — is emerging in biopharma.
For operators and engineers, the role will evolve: from executing to collaborating with intelligent systems — interpreting, validating and co-deciding. Organizations that master this human-machine symbiosis will gain operational resilience, agility and regulatory robustness.
Conclusion
In biologics manufacturing, where complexity, variability and regulatory demands are high, digital twins are no longer optional — they are a strategic necessity. But technology alone doesn’t deliver value. The true power lies in human-machine collaboration: operators working with digital twins to foresee issues, make decisions, and execute with confidence.
For manufacturers and CDMOs focused on operational excellence, building an integrated twin + human interface is a pathway to better yields, fewer deviations, faster scale-up, and stronger regulatory readiness.
Is your team ready to make the leap? The twin is ready — ensure your operators are too.
QxP Vice President Christine Feaster is a 20+ year veteran in pharma quality assurance. Prior to joining QxP, Christine was a vice president of U.S. Pharmacopeia.
