The first point, Master Data Accuracy, seems trivial but it is not. Indeed, any
digital solution becomes useless if data is not correct.
The second point implies the continuous “re-skilling” and “up-skilling” of the
supply chain people as new skills and mindset are required.
Effective Change Management is the crossroad between the success and failure of
any digital transformation.
We live in a “VUCA” (Volatility, Uncertainty, Complexity, Ambiguity) world
where tons of real-time data, better processing power, and more sophisticated
analytical algorithms are starting to create seismic shifts in how the supply chain
will operate today and in the future.
7 Digital Twin: Case Study from Sanofi
7.1 Objective
This initiative was developed by Sanofi and partially demonstrated at the Usine
Extraordinaire event took place in Paris in November 2018.
The objective was to improve operations of a cell culture production line with an
endpoint goal of reducing operational incidents leading to lost batches or release
delays and to improve overall productivity and yield of production.
7.2 Challenges
The two key challenges are (1) the creation of a model that could reliably simulate
the biomanufacturing processes, and (2) ensuring that the operational teams integrate
the use of the digital twin in daily activities and are confident with the outcome of the
virtual model.
For the first challenge, the main barrier quickly became the availability and
quality of data. As we started building models it became very apparent that the
data being used to run an industrial process on a daily basis was insufficient to train
and validate a model that could reliably replicate the process.
For the second challenge, the issue was different and caused the project to change
direction somewhat. While the initial focus was very much on the simulation model,
working with the operators it became clear we needed to take a much broader
approach where the digital twin was not just a sophisticated technology for a few
experts but a solution for the operators in their complete lifecycle experience from
training to operations and continuous improvement. Creating operational impact
from the digital twin required true operational familiarity and trust from the operational teams.
Digital Twins: A General Overview of the Biopharma Industry
181
digital solution becomes useless if data is not correct.
The second point implies the continuous “re-skilling” and “up-skilling” of the
supply chain people as new skills and mindset are required.
Effective Change Management is the crossroad between the success and failure of
any digital transformation.
We live in a “VUCA” (Volatility, Uncertainty, Complexity, Ambiguity) world
where tons of real-time data, better processing power, and more sophisticated
analytical algorithms are starting to create seismic shifts in how the supply chain
will operate today and in the future.
7 Digital Twin: Case Study from Sanofi
7.1 Objective
This initiative was developed by Sanofi and partially demonstrated at the Usine
Extraordinaire event took place in Paris in November 2018.
The objective was to improve operations of a cell culture production line with an
endpoint goal of reducing operational incidents leading to lost batches or release
delays and to improve overall productivity and yield of production.
7.2 Challenges
The two key challenges are (1) the creation of a model that could reliably simulate
the biomanufacturing processes, and (2) ensuring that the operational teams integrate
the use of the digital twin in daily activities and are confident with the outcome of the
virtual model.
For the first challenge, the main barrier quickly became the availability and
quality of data. As we started building models it became very apparent that the
data being used to run an industrial process on a daily basis was insufficient to train
and validate a model that could reliably replicate the process.
For the second challenge, the issue was different and caused the project to change
direction somewhat. While the initial focus was very much on the simulation model,
working with the operators it became clear we needed to take a much broader
approach where the digital twin was not just a sophisticated technology for a few
experts but a solution for the operators in their complete lifecycle experience from
training to operations and continuous improvement. Creating operational impact
from the digital twin required true operational familiarity and trust from the operational teams.
Digital Twins: A General Overview of the Biopharma Industry
181