Developing formal ontologies for the key data domains of the twin and leveraging
existing standard taxonomies is the current good practice for addressing this area [7].
The field is still emerging, so overcoming the two barriers of finding the right skill
sets and building on solid initiatives that will become industry standards is a key
challenge. An example of an initiative with such a potential is allotrope.org which is
building a complete framework of ontologies, taxonomies, and data modeling for
analytical test data.
Failing to do the appropriate effort in the early stages of a twin project might save
some time but as the twin model starts to evolve the increasing load of managing
changes to data model versions and realigning datasets to enable analysis across
experiments quickly becomes unsustainable and may completely break the project.
3.9 People
All projects require people of course but digital twins have specific constraints.
Looking at the list of prerequisites, you see that a lot of different competencies are
required to be put together and collaborate efficiently. Also, many of these experts
will need to work slightly outside their traditional comfort, some because the
interdependencies of the digital twin create additional stress on the technologies
and domains being applied.
Beyond this, there are also major mindset changes required to be successful. You
need people to trust data coming from the twin to change how they operate things.
This is less obvious than it might seem in many operational environments. Another
change in behavior that a digital twin will require is to move from an approach of
“Tests ! Hypothesis ! Confirmation” to an approach of “Simulation ! Hypothesis ! Confirmation Test.”
4 Typical Lifecycle of a Twin
Building digital twins is complex, as it requires a deep understanding of the physical
entity and the synchronization of the virtual and physical side of the twin. That is
why digital twins generally go through iterations. If we investigate the case of digital
twins for industrial assets (production equipment, factories, supply chain,etc.) the
first step will generally start while designing a physical entity using CAD-type tools.
A relatively limited additional investment connecting sensor data to this virtual
model will then deliver a basic twin. In most cases, however, as the physical entity
enters operational life the two components will get out of sync and the digital twin
falls apart.
The second phase will then often be the development of a digital twin on an
existing physical phenomenon. The focus then becomes much more on the design of
a virtual model of this, both the 3D type modeling and more importantly dynamic
Digital Twins: A General Overview of the Biopharma Industry
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existing standard taxonomies is the current good practice for addressing this area [7].
The field is still emerging, so overcoming the two barriers of finding the right skill
sets and building on solid initiatives that will become industry standards is a key
challenge. An example of an initiative with such a potential is allotrope.org which is
building a complete framework of ontologies, taxonomies, and data modeling for
analytical test data.
Failing to do the appropriate effort in the early stages of a twin project might save
some time but as the twin model starts to evolve the increasing load of managing
changes to data model versions and realigning datasets to enable analysis across
experiments quickly becomes unsustainable and may completely break the project.
3.9 People
All projects require people of course but digital twins have specific constraints.
Looking at the list of prerequisites, you see that a lot of different competencies are
required to be put together and collaborate efficiently. Also, many of these experts
will need to work slightly outside their traditional comfort, some because the
interdependencies of the digital twin create additional stress on the technologies
and domains being applied.
Beyond this, there are also major mindset changes required to be successful. You
need people to trust data coming from the twin to change how they operate things.
This is less obvious than it might seem in many operational environments. Another
change in behavior that a digital twin will require is to move from an approach of
“Tests ! Hypothesis ! Confirmation” to an approach of “Simulation ! Hypothesis ! Confirmation Test.”
4 Typical Lifecycle of a Twin
Building digital twins is complex, as it requires a deep understanding of the physical
entity and the synchronization of the virtual and physical side of the twin. That is
why digital twins generally go through iterations. If we investigate the case of digital
twins for industrial assets (production equipment, factories, supply chain,etc.) the
first step will generally start while designing a physical entity using CAD-type tools.
A relatively limited additional investment connecting sensor data to this virtual
model will then deliver a basic twin. In most cases, however, as the physical entity
enters operational life the two components will get out of sync and the digital twin
falls apart.
The second phase will then often be the development of a digital twin on an
existing physical phenomenon. The focus then becomes much more on the design of
a virtual model of this, both the 3D type modeling and more importantly dynamic
Digital Twins: A General Overview of the Biopharma Industry
175