- When I speak of the build phase,
I'm referring to both the construction
of products and systems
and also to the systems that manufacture products.
In both areas, digital twins are beginning to play
a major role.
In design, a digital twin is used to create
the optimum solution.
It is the result of exhaustive simulations and rich data
that specify areas such as best architecture,
configuration, materials, and cost.
More advanced digital twins can produce specifications
for the manufacturing process,
including the production line,
the metrics that must be captured during build,
and testing procedures for parts and the end product.
As you can imagine, it's really helpful to understand
how a production line might perform for a given product
before that production line is built.
A digitally-enabled manufacturing plant,
called a smart factory,
may have digital twins from design
through to fully manufactured products,
and even to post-deployment performance
and maintenance in the field.
This is the end-state of comprehensive
product lifecycle management or PLM.
Let's turn our focus to digital twins
in the operations of a manufacturing facility.
The virtual replicas of all the relevant physical parts
of the production facility that we want to monitor
and manage are called manufacturing digital twins.
The more devices that have sensors
for sending back to their respective digital twins,
the greater the ability to model and improve
the entire process.
This sensor network is collectively referred to
as an Industrial Internet of Things or IIOT.
We can think of this IIOT digital twin architecture
in layers, depending on business need.
For example, some stakeholders monitoring could reduce data
of high-level metrics for multiple factories
or a single factory.
This is Level 3, the Enterprise Business level.
For others, it could be to gain visibility
to one or more production lines.
This is Level 2, the Operational Process level.
Finally, gleaning information of individual pieces
of equipment is Level 1, the Assets level.
Depending on need and granularity of analysis,
the manufacturing digital twin provides visualization
and data on the performance of various aspects
of the product lifecycle management.
It can highlight where process improvements are required
and where resources should be directed,
either in real time or in the future.
It can inform an overall equipment efficiency, or OEE,
and issues such as mean time between failure, or MTBF.
A digital twin can use artificial intelligence
to perform predictive maintenance
at any point in the production line.
Anticipating an issue and fixing before a problem
is always the preferred option.
These insights are enormously valuable
to an organization's stakeholders.
But they may be useful to others, too.
Depending on relationships,
manufacturing digital twins, or just their data,
can be shared with external providers,
maintenance companies, regulators, and even buyers.
This provides important collaboration opportunities
and can improve the entire supply chain.
Everyone has a vested interest
in performance efficiencies and quality.
Manufacturing digital twins are playing
an important role in the transformation
of how products and systems are being built.
They are enabling Industry 4.0.