7.3 Digital Twin Solution
The end approach consisted of (1) building a detailed 3D CAD model of the
production line facility and equipment, (2) designing a simulation model for each
of the production steps based on historical runs of the same process using key
production parameters and measurements and that could predict end-stage values
of key outcomes (yield and failed batch) in the initial phases of the step, and
(3) connecting sensor data from the process and analytical tests to the model to
visualize in context of the equipment.
A first use case was then averaging this model to develop training modules where
operators could learn how to operate the process and by themselves use the simulation model to understand and learn how operations parameters and decisions
impacted the outcome of the biomanufacturing process.
This training experience then makes it much easier to leverage the twin in
operational conditions because the environment and types of decisions are already
familiar. Not only does the 3D model provide better training on how to operate an
equipment but better understanding of what is happening in the bioprocess increases
the right operational procedures.
The second use case was focused on providing shared real-time information on
production process status. We confirmed that in many cases the key data on
production status is not broadly shared, both because of basic information access
and because that data is often managed in technical environments requiring very
expert skills to understand its meaning. Providing easily understandable data in the
context of the twin is a key enabler in creating the shared understanding of status that
avoids operational issues caused by misunderstandings, lack of information, or
erroneous data transmission.
The third use case was to enable a set of key decisions on the shop-floor with
outcomes of twin predictions. The three main decisions targeted were:
• Decision to stop a batch early that would fail thereby enabling production to save
time and quickly restart a new batch
• Decision on timing to end a batch when optimal yield/duration has been hit
• Decision to adjust process control parameters (within the specification of course)
in order to optimize yield or avoid deviations and lost batches
7.4 Lessons Learnt
As stated above, integrating the human element of operations was a critical factor in
the direction that the twin project took. Creating the visual element of a 3D model
and training experiences was a key enabler in operations buy-in and support for the
more advanced use cases.
The human factor also drives the need to clearly define and prioritize the
operational decisions that the twin is aimed at enabling. It is of course possible to
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M. Canzoneri et al.
The end approach consisted of (1) building a detailed 3D CAD model of the
production line facility and equipment, (2) designing a simulation model for each
of the production steps based on historical runs of the same process using key
production parameters and measurements and that could predict end-stage values
of key outcomes (yield and failed batch) in the initial phases of the step, and
(3) connecting sensor data from the process and analytical tests to the model to
visualize in context of the equipment.
A first use case was then averaging this model to develop training modules where
operators could learn how to operate the process and by themselves use the simulation model to understand and learn how operations parameters and decisions
impacted the outcome of the biomanufacturing process.
This training experience then makes it much easier to leverage the twin in
operational conditions because the environment and types of decisions are already
familiar. Not only does the 3D model provide better training on how to operate an
equipment but better understanding of what is happening in the bioprocess increases
the right operational procedures.
The second use case was focused on providing shared real-time information on
production process status. We confirmed that in many cases the key data on
production status is not broadly shared, both because of basic information access
and because that data is often managed in technical environments requiring very
expert skills to understand its meaning. Providing easily understandable data in the
context of the twin is a key enabler in creating the shared understanding of status that
avoids operational issues caused by misunderstandings, lack of information, or
erroneous data transmission.
The third use case was to enable a set of key decisions on the shop-floor with
outcomes of twin predictions. The three main decisions targeted were:
• Decision to stop a batch early that would fail thereby enabling production to save
time and quickly restart a new batch
• Decision on timing to end a batch when optimal yield/duration has been hit
• Decision to adjust process control parameters (within the specification of course)
in order to optimize yield or avoid deviations and lost batches
7.4 Lessons Learnt
As stated above, integrating the human element of operations was a critical factor in
the direction that the twin project took. Creating the visual element of a 3D model
and training experiences was a key enabler in operations buy-in and support for the
more advanced use cases.
The human factor also drives the need to clearly define and prioritize the
operational decisions that the twin is aimed at enabling. It is of course possible to
182
M. Canzoneri et al.