These days, Digital Twin is under the spotlight of the biomanufacturing industry when it comes to industry 4.0. 

In this week’s blog post, we’ll share some thoughts about the challenges of the biomanufacturing industry in their digitalisation efforts. We will then see how the Digital Twin can become key to predict and optimise process behaviour and product quality, across the whole product lifecycle.  

If you are curious about it, keep reading! 

So, why would we need a Digital Twin? 

Despite the similarity with the other manufacturing processes, the biomanufacturing industry has some specific challenges that have an impact in their digitalisation processes, such as: 

  • Intrinsic complexity and variability of bioprocesses (e.g., dealing with the requirements of cell-based products).
  • The complexity of unit operations and core processes (e.g., CQAs are difficult to measure).
  • The range of equipment setups and operation modes. 
  • Great variety of production processes (e.g., specific kinetics and unique challenges).
  • The cost of producing a single data point, which leads to the generation of small datasets. 
  • Compliance with ISO standards, GMP/GLP validation, and international regulatory guidelines. 

This means that, notwithstanding the exciting potential benefits of adopting industry 4.0, there is still a long way to go to implement and benefit from it.  

We believe that one way to overcome these challenges is the implementation of Digital Twins. The use of this type of technology will facilitate the collaboration and the information management across the whole organisation. 

But how can the Digital Twin make a difference? 

First, you need to know what Digital Twin is! 

According to the literature, a Digital Twin “consists of a virtual representation of a production system that is able to run on different simulation disciplines that is characterized by the synchronization between the virtual and real system, thanks to sensed data and connected smart devices, mathematical models and real time data elaboration” 1

How do they work? Well, in Digital Twins, the automated data flow goes from physical to digital and from the digital to the physical domain, using models that recreate and record real process properties and behaviours.

Because of that, a Digital Twin allows you to simulate the interaction of different units. Therefore, it makes it easier to make adaptative decisions in your processes. 

At the same time, a Digital Twin enables you to acquire real-time knowledge and act on it. Which translates into efficiently reducing experimental efforts, reuse and share protocols and models with less consumption of resources and time. 

To conclude, the Digital Twin is a digital version of the system based on high-fidelity models that provide deep insights through simulation. It’s a more sophisticated and smarter way of using data in production digitalisation and optimisation.  

The capability of collecting, managing and analysing data from different sources and using it for diagnostics and forecasting is a significant and powerful application of digital solutions in biopharma.  This brings many exciting advantages such as: 

  • Process and Product knowledge are enhanced. 
  • Quality Control is further automatised. 
  • Decision-making is more streamlined. 
  • Optimisation and Troubleshooting efforts are accelerated. 

And what is important to consider in your DT Implementation? 

If you are thinking about a DT implementation, the first requirement for its success is to have a clear scope and an objective for its implementation. 

Then, it is important to have an extensive knowledge of interconnected data to generate and/or manage a data lake. Lastly, you should have bioprocess experts with a complete understanding of data processing. Why? Well, these experts will evaluate the feasibility of the identified trends and overseeing the Digital Twins while it operates. 

 addition, these experts must be able to interpret the data collected from the process and understand, create, and improve mathematical models, and finally to interpret the outcome of such models to streamline decision making and act upon it faster and more accurately.

We can help your Digital Twin Implementation

Biomanufacturing is going through exciting times! If you would like to implement Digital Twin in your processes, we can help you! 

Have you imagined how faster and structured would be your processes with that? Take a look at 4TE services and don’t hesitate to contact us. 

References

(1) Garreti M, Rosa P, Terzi S (2012) Life cycle simulation for the design of product-service systems. Comput Indust 63(4):361-369