Optimizing Human-Machine Collaboration When Using Digital Twins in Global Biopharma Manufacturing

The global biopharmaceuticals market is projected to grow from its current valuation of $666.4 billion USD to $1.2 trillion USD within the next 8 years (Fortune, 2025). This growing global demand for biopharmaceuticals has fueled the need for technology solutions that improve scalability and challenged global regulatory agencies to provide guidance and keep up with approvals in this dynamic environment.

In addition, advances in artificial intelligence (AI) and other technologies have lowered the barrier to entry for implementing digital twins, fueling innovation in biopharma manufacturing and allowing companies to experience greater scalability, while reducing costs and time to market. To maximize these benefits, smart organizations must pay close attention to human-machine collaboration throughout the design and implementation process. For global biopharma manufacturers, this means designing the digital twin with the world in mind. 

While human-machine collaboration affects every aspect of the digital twin, three areas are particularly important:

• System design
• Human-machine collaboration design (UI/UX) and training
• Global regulatory environment

System Design

Digital twins offer a virtual representation of a physical process, featuring bi-directional communication between the two. As such, digital twins in biopharma manufacturing fall under the regulations for type III high-level models (Schmidt, et al., 2025). This designation highlights the importance of engaging stakeholders early in the design process, assessing internationalization and localization requirements, and establishing validation criteria to ensure regulatory approval.

Stakeholder Engagement

Digital twins are primarily internal tools used for optimizing and scaling the biopharma manufacturing process, but they have a significant impact on business operations. As with any large project, it is vital to recognize and plan for the human change management aspect of this innovation. Engaging stakeholders early and in a way that focuses on the organization’s strengths gives everyone ownership in the project’s success, which can alleviate some of the challenges that come with any significant change. 

Internationalization and Localization Requirements

Getting this part right is foundational to having a digital twin that effectively supports human-machine collaboration and facilitates the adoption of tools. 

When you design with the world in mind (internationalization), it means that you are building in multi-cultural support from the beginning and setting up the software in a way that facilitates localization and accessibility. It’s always easier and less expensive to design globally from the beginning than it is to retrofit later.

Involving your localization partner early in the design process can help prevent expensive mistakes during implementation. In the design phase, your localization partner can assist you in evaluating which software tools best support internationalization and localization.

Model Validation

The model drives much of the automation and decision support for the digital twin, so it is critically important that the model is adequately documented and rigorously validated against accepted industry frameworks, such as the in silico trials framework (Viceconti, 2021). 

Additionally, regulatory agencies may have locale-specific requirements for validation. It is advisable to work with local regulatory professionals and a knowledgeable life sciences LSP to ensure that you meet the requirements for that locale and your particular implementation. 

Data Management and Communication

Real-time data informs the model in a true digital twin, and the resulting insights inform the physical system. This allows a feedback loop of continuous optimization and error detection, which in turn provides robust decision support for human operators. This system depends on reliable data integration, low latency in the feedback loop, and clear process controls.

Process Controls

Biopharma manufacturing is process-heavy, with multiple points of human-machine collaboration. The manufacturing and distribution processes must also follow strict chain-of-custody protocols and regulatory requirements. If this environment is not properly calibrated and managed, it can potentially be rife with opportunities for error. 

An effective digital twin is not only regulatory-aware but also alleviates the human operator’s cognitive workload with appropriate levels of automation (e.g., evaluation, resource allocation, traceability, and change control).

Human-Machine Collaboration Design (UI/UX)

These projects have a way of exacerbating existing problems at the intersection of people, process, and technology. While technology will not solve people and process problems, exposing the challenges provides an opportunity for the team to fix or mitigate them.

The system design provides the scaffolding to support new ways of working with the interface between human and machine. Where the digital twin intersects the physical world and the human operator, innovative biopharma manufacturers carefully consider the quality of that interaction.

One criticism of AI is the tendency for the model to be a “black box.” With digital twins, transparency and explainability help build trust for the human operator. However, there is also a need to balance model and decision transparency with managing the cognitive workload in these fast-paced environments.

The best systems make it easy for the human operators to do the right thing and hard to do the wrong thing. This is particularly important in biopharma manufacturing. 

Here are some key areas where thoughtful implementation can have a considerable impact:

• Clearly defined roles and responsibilities for the human-machine interactions. Well-defined and documented roles and responsibilities build trust and ensure that the system maximizes the strengths of both the human operator and the digital twin. Clear delineation also builds confidence and facilitates learning. This results in improved efficiency and less downtime.
• Cognitive workload management. In process-heavy environments like biopharmaceutical manufacturing, human operators must maintain constant awareness of multiple aspects of the system to make timely decisions. If the cognitive workload gets too high, the operator becomes more prone to errors. Carefully consider how the design of the digital twin can reduce cognitive load while providing accurate and timely information about the system.
• Transparency and explainability. When the logic, data, calculations, and processes are transparent and explainable, operators become more confident in their decision-making (Shahab, et al., 2025). When operators can easily interpret system feedback, they can make better decisions more quickly, improving overall system performance. 
• Trust calibration. Related to transparency, operators need to understand both the strengths and limitations of the digital twin, so that they can better trust the information they receive. Debiasing data, curating content, receiving real-time feedback on reliability, and creating a consistent interface also contribute to trust. 
• Task-oriented and consistent UI/UX. Effective decision support systems like digital twins present information in context, at the right time, in the right format, so that the operator can make use of it. In addition, effective design means being consistent with how related information gets displayed (e.g., alarms using a consistent tone and color, related items grouped on the dashboard for easier interpretation, consistent and culturally appropriate colors, etc.)
• Appropriate and adjustable levels of automation. Allowing operators to adjust the level of support they receive from the system can help operators retain situational awareness and control. Predictive suggestions can reduce cognitive load and improve performance. Such flexibility also facilitates competency development and continuous learning.
• Ergonomic displays and physical components. Repetitive strain and other injuries are common in manufacturing environments. To reduce the physical strain on the operator, carefully evaluate the workload, posture, and cognitive demand, and then make adjustments to ensure worker safety and comfort.
• Operator training. Humans learn best by doing. Mixed reality simulations using the digital twin can help operators become more competent more quickly, while receiving culturally appropriate instruction in the operator’s native language facilitates comprehension and retention of complex topics. Post-training assessment evaluates the success of the training program and facilitates continuous improvement. (Shahab, 2025; Schmidt, 2025; Wilhelm, 2021)

Global Regulatory Environment

Global regulatory agencies and standards organizations often struggle to keep up with rapidly evolving technology. Digital twins in biopharma manufacturing are considered type III high-level models (Schmidt, 2025). As such, they are governed by multiple regulations related to data security and integrity, model validation and calibration, Good Manufacturing Practices, and Good Machine Learning Practices. Several regulatory agencies, including the FDA and EMA, have developed initial guidance on using AI and digital twins. 

Good documentation of processes and model validation can facilitate regulatory approval. Most agencies require submissions to be localized for the local culture, laws, and language. 

Conclusion

Digital twins facilitate rapid prototyping, process optimization, real-time analysis, and error correction, providing scalability in a growing industry. 

Designing with the world in mind and localizing the digital twin helps maximize the effectiveness of human-machine collaboration, facilitates scalability, reduces time to market, and improves safety and efficiency. 

These improvements fuel biopharma manufacturing innovation, ultimately leading to lower-cost pharmaceuticals and a healthier world. Connect with Vistatec Life Sciences to discover how language strategy can help to support and drive innovation at every level of your operations.