Abstract This chapter gives an industry perspective of how digital twins are
tangibly translated, implemented, and used in a biopharmaceutical environment.
Technical prerequisites and components including data modeling, the lifecycle,
and different skills which are required from people to be put together and collaborate
efficiently with digital twins are discussed with practical examples which have been
implemented in labs and in manufacturing.
Keywords 3D model, Biopharma industry, Connectivity, Contextualizing data,
Critical process parameters (CPP), Critical quality attributes (CQA), Data, Digital
twin, Healthcare, Holistic, Human factor, Laboratory, Lifecycle, Logistics,
Manufacturing, Ontologies, Operations, People, Physical asset, Predictive,
Prescriptive, Quality by design (Qbd), Research and development (R&D),
Self-driving, Sensors, Simulation, Supply chain, Taxonomies, Virtual Model,
VUCA
1 Introduction
A digital twin is the result of the convergence of two coexisting systems, the tangible
and real system of a living organism or a nonliving physical entity and its virtual
replica which is enabled by real-time data and underlying models through the use of
digital technologies.
Digital twins have the ability to provide a holistic understanding of the system by
building a network of dependencies between real-time data and their underlying
meta information.
Through the use of Internet of things (IoT), advanced data analytics, artificial
intelligence (e.g., machine learning, deep learning), and models (descriptive, predictive, and prescriptive) the digital twin becomes a living replicate of the physical
entity which adapts to real-time information coming from its originator. In the scope
of Biopharma, physical entities can range from a biological cell to a complete factory
or supply chain. Models should be formalized either as mathematical models or
algorithms in order to enable simulations.
So, considering a digital twin to be a real-time connected virtual-physical system
where physical reality and virtual models are continuously connected, we will
broadly have different levels of twins:
• The most basic digital twin establishes a one-directional relationship between a
fixed physical entity and a fixed virtual model providing real-time transmission of
physical parameters to the virtual environment for visualization, analysis, and
experimental design. Each side of the digital twin system is fixed in the sense that
only parameter values change. For instance, a digital twin of a bioreactor would
be fixed in the sense that the bioreactor does not change while parameters such as
cell density, temperature, pH, etc., would change.
• A more realistic approach in operational usage requires the virtual-physical
system to remain operational as changes are made to the structure either of the
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tangibly translated, implemented, and used in a biopharmaceutical environment.
Technical prerequisites and components including data modeling, the lifecycle,
and different skills which are required from people to be put together and collaborate
efficiently with digital twins are discussed with practical examples which have been
implemented in labs and in manufacturing.
Keywords 3D model, Biopharma industry, Connectivity, Contextualizing data,
Critical process parameters (CPP), Critical quality attributes (CQA), Data, Digital
twin, Healthcare, Holistic, Human factor, Laboratory, Lifecycle, Logistics,
Manufacturing, Ontologies, Operations, People, Physical asset, Predictive,
Prescriptive, Quality by design (Qbd), Research and development (R&D),
Self-driving, Sensors, Simulation, Supply chain, Taxonomies, Virtual Model,
VUCA
1 Introduction
A digital twin is the result of the convergence of two coexisting systems, the tangible
and real system of a living organism or a nonliving physical entity and its virtual
replica which is enabled by real-time data and underlying models through the use of
digital technologies.
Digital twins have the ability to provide a holistic understanding of the system by
building a network of dependencies between real-time data and their underlying
meta information.
Through the use of Internet of things (IoT), advanced data analytics, artificial
intelligence (e.g., machine learning, deep learning), and models (descriptive, predictive, and prescriptive) the digital twin becomes a living replicate of the physical
entity which adapts to real-time information coming from its originator. In the scope
of Biopharma, physical entities can range from a biological cell to a complete factory
or supply chain. Models should be formalized either as mathematical models or
algorithms in order to enable simulations.
So, considering a digital twin to be a real-time connected virtual-physical system
where physical reality and virtual models are continuously connected, we will
broadly have different levels of twins:
• The most basic digital twin establishes a one-directional relationship between a
fixed physical entity and a fixed virtual model providing real-time transmission of
physical parameters to the virtual environment for visualization, analysis, and
experimental design. Each side of the digital twin system is fixed in the sense that
only parameter values change. For instance, a digital twin of a bioreactor would
be fixed in the sense that the bioreactor does not change while parameters such as
cell density, temperature, pH, etc., would change.
• A more realistic approach in operational usage requires the virtual-physical
system to remain operational as changes are made to the structure either of the
168
M. Canzoneri et al.