Multiple technologies have emerged
in recent years that are instrumental
in driving the advance of
smart manufacturing and the Industrial IoT
(Internet of Things). These include advanced
analytics, artificial intelligence (AI) and
machine learning (ML), operational intelligence,
advanced robotics, cyber-physical
systems, and generative design for additive
manufacturing. While each of these technologies
is changing the face of manufacturing
today, ARC Advisory Group research
suggests that the Industrial IoT, connected
smart assets, and the digital twin are having
the most immediate and significant impact
on how companies implement technologies
that enable smart manufacturing.
How the digital twin is
being implemented today
An integral component of a digital twin of
a production system is the virtual model of
the real-world products, assets, and processes.
Virtual modeling provides manufacturing
engineers with the ability to simulate
and model the virtual and the physical,
simultaneously or separately. This digital
twin system modeling approach enables
them to understand the holistic nature of
their assets and production systems within
the overall manufacturing ecosystem. Further,
we are seeing the emergence of powerful
digital twin development tools offered
by suppliers that will enable manufacturers
to understand exactly how their factory systems
and equipment function, and enable
them to make decisions to enhance performance
and product quality through human
and artificial intelligence.
Digital twins can be applied to discrete
manufacturing ecosystems in three distinct
areas: product, production, and performance.
The product digital twin is used to enable
more efficient design and improve the product.
In some cases, the product is the actual
equipment and assets used in the production
system. Virtual simulation modeling can validate
product performance, while simulating
how the product is currently behaving in
a physical environment. This provides the
product developer with a physical-virtual
connection that allows them to analyze how
a product performs under various conditions
and make changes in the virtual design
model to ensure that the physical product
will perform as designed in the field. This
eliminates the need for physical prototypes
and reduces development time.
Production digital twins are used in manufacturing
and production planning. They can
help to validate how well a manufacturing
process will perform on the shop floor before
the physical production equipment and work
cells go into actual production. Today, the virtual
commissioning of production automation
—an established technology and process—is
merging with the more expansive scope of
the digital twin. Virtual commissioning is typically
a one-time validation of an automated
production system. In contrast, the digital
twin represents an ongoing analytical and
optimizing process that takes place in real
time. By simulating the production process
using a digital twin and analyzing the physical
events across the digital thread, manufacturers
can create a production environment that
remains efficient under variable conditions.
Performance digital twins are used to capture,
analyze, and act on operational data.
An important initial step when developing
and implementing a digital twin is to identify
the exact operational configuration of the
product, asset, or production equipment that
represent the physical components.
Context and configurational
data required
When implementing, user companies need
to include context within the digital twin
configuration. For predictive analytics or
industrial IoT to be effective, the context
(physical configuration) of the asset and
system are required to know exactly what is
needed to collect the relevant operational
and performance data. Companies implementing
any digital twin project should begin
by capturing and managing the actual physical
configuration of the asset. Additionally,
due to the many use cases for a digital twin
across the product lifecycle, implementers
would be well served to employ digital twin
technology that can integrate a flexible/
dynamic data model.
The operational element of the digital
twin aligns closely with concepts and technologies
associated with industrial IoT. While
virtual CAD models and product performance
simulations define the fit, form, and
function of the product, the real-time and
operational data is the digital output of the
physical assets in operation. The information
is captured through sensors, industrial IoT
endpoints, and intelligent edge devices in
real time. This connects the digital twin to
physical reality. Combined with the various
forms of contextual data, this knowledge
provides a foundation for insightful and
timely decision-making, leading to process
improvement and optimization.
Recommendations
To realize meaningful benefits when implementing
a digital twin for smart manufacturing,
organizations must think holistically. Successful
implementation is much more than
engineering design models or mechanical and
electric components of a production work
cell. The entire digital twin system must be
modeled based on the virtual and physical
elements and the desired output and results.
For a digital twin, analytics, and operational
performance to be effective, the context
of the product or asset within the system is
required. The ultimate benefits of a digital
twin can be quantified by understanding up
front that physical configuration is just as
important as virtual design.
