3.5 Configuration Management
Managing change is another prerequisite for a successful digital twin. It should be
obvious by now that a digital twin requires the synchronization of a lot of independently moving parts. While this is of course theoretically possible to do manually it
quickly becomes complex and error prone.
Ensuring that changes are appropriately validated with the right workflows, that
adequate impact analysis is done beforehand, and that consistent configurations of
stable states are managed is a job by itself [5]. It will also typically require a
dedicated solution. Some companies have managed to develop this as a dedicated
solution but in most cases using a standard solution from the PLM world will be the
best answer.
No solution is perfect and not in this case either. Leveraging PLM solutions
usually developed in discrete industries has benefits but adapting these to the
conditions of a Biopharma industry remains a major challenge.
3.6 Dynamic Model
Up to now, we have remained within the limited ambition of having a virtual model
that continuously represents the physical environment. As we move beyond that into
an area where the virtual models start to have an autonomous existence and influence
on the physical environment, we start to create new prerequisites.
The virtual models need to be able to evolve by itself and influence backwards to
the physical world, which essentially means that it needs to contain a model whereby
it can:
• Enable simulations of system behavior without input from the physical environment. This will usually for a digital twin include a combination of mechanistic
and algorithmic models to cover behaviors of the physical assets and biopharmaceutical processes.
• Drive changes in the physical environment based on measurements and predictive simulations. This will normally encompass a sophisticated process control
strategy and model that defines how you can interact with the physical reality.
The dynamic model is the combination of the two. Unsurprisingly this is a much
more open area where few solutions exist. Yet, having the depth of equipment and
process understanding required to design such models and control strategy is a
condition for building a useful digital twin. This is the moment where we start
using the digital twin not just for information but to actually influence operational
outcomes [6].
There are many potential pitfalls in this step. The standard process control
standards in operations today are far from the level required to be useful in a digital
twin context. Process characterization, understanding, and control will not be at the
Digital Twins: A General Overview of the Biopharma Industry
173
Managing change is another prerequisite for a successful digital twin. It should be
obvious by now that a digital twin requires the synchronization of a lot of independently moving parts. While this is of course theoretically possible to do manually it
quickly becomes complex and error prone.
Ensuring that changes are appropriately validated with the right workflows, that
adequate impact analysis is done beforehand, and that consistent configurations of
stable states are managed is a job by itself [5]. It will also typically require a
dedicated solution. Some companies have managed to develop this as a dedicated
solution but in most cases using a standard solution from the PLM world will be the
best answer.
No solution is perfect and not in this case either. Leveraging PLM solutions
usually developed in discrete industries has benefits but adapting these to the
conditions of a Biopharma industry remains a major challenge.
3.6 Dynamic Model
Up to now, we have remained within the limited ambition of having a virtual model
that continuously represents the physical environment. As we move beyond that into
an area where the virtual models start to have an autonomous existence and influence
on the physical environment, we start to create new prerequisites.
The virtual models need to be able to evolve by itself and influence backwards to
the physical world, which essentially means that it needs to contain a model whereby
it can:
• Enable simulations of system behavior without input from the physical environment. This will usually for a digital twin include a combination of mechanistic
and algorithmic models to cover behaviors of the physical assets and biopharmaceutical processes.
• Drive changes in the physical environment based on measurements and predictive simulations. This will normally encompass a sophisticated process control
strategy and model that defines how you can interact with the physical reality.
The dynamic model is the combination of the two. Unsurprisingly this is a much
more open area where few solutions exist. Yet, having the depth of equipment and
process understanding required to design such models and control strategy is a
condition for building a useful digital twin. This is the moment where we start
using the digital twin not just for information but to actually influence operational
outcomes [6].
There are many potential pitfalls in this step. The standard process control
standards in operations today are far from the level required to be useful in a digital
twin context. Process characterization, understanding, and control will not be at the
Digital Twins: A General Overview of the Biopharma Industry
173