How does data become knowledge
and actionable insight? Usually, it’s
a question of context. Manufacturing
data without context is, ultimately, just
data—often overwhelming in volume, hard
to interpret, and difficult to use in practice.
For manufacturing businesses, this can
be a day-to-day challenge. That 75° reading
from a temperature sensor is meaningless
unless you know which line it’s on, which
product it’s producing, what ambient conditions
exist in the plant, what range of
readings is expected, what historical track
record it has for accuracy, and so on.
While existing control systems can be
configured to raise an alarm if readings
exceed predefined thresholds, they typically
still fail to provide that extra context,
especially when important maintenance
information is hidden away in unstructured
reports, diagrams, or pictures.
The missing link between
data and insight
The key question for manufacturers is
therefore how to get this much-needed
context to turn raw data into actionable
insight—and do so with automation that
brings the scale and pace required by modern
manufacturing plants. This, ultimately,
is how to unlock the highest-value data-driven
use cases, in areas like predictive
maintenance and autonomous operations.
It’s true that recent years have seen real
progress in manufacturing data architectures.
In particular, many businesses have
been investing in data lakes. By bringing
manufacturing data together in one place,
these have proved to be a powerful way of
overcoming traditional siloed data architectures
and releasing key data that was
previously locked away in spreadsheets or
fragmented systems.
However, data lakes by themselves cannot
typically provide the critical, extra context
required to unlock value. In particular,
they generally fall short of providing the
structured information and insights that
engineers and operators need to actually
improve manufacturing operations.
Digital twins as a solution
The real game-changer here is the digital
twin. By creating real-time virtual representations
of physical systems, digital
twins not only bring data together from
multiple sources, but also unify and contextualize
that data.
In effect, this provides a ‘one-stop-shop’
resource of contextualized manufacturing
data for business users, engineers, operators,
and even other industrial applications
and algorithms to use. This, in turn,
enables new use cases such as supply chain
and production simulation, and predictive
intelligence at scale.
One of the most compelling aspects of a
digital twin is its ability to store and structure
information in a way engineers and
operators can understand. This is important
because having to consult a data analyst
every time you want to understand or
use a data set is simply not an efficient or
sustainable solution.
Digital twins can also support faster and
more accessible application development
to solve day-to-day manufacturing challenges.
For example, by adding modern
low code/no code (LCNC) tools to the mix,
manufacturing businesses can give their
data engineers an intuitive and safe space
for experimenting with new ways to optimize
operations and ultimately improve
quality, throughput, and efficiency.
A key step towards
autonomous operations
Another important benefit of the digital
twin is the way it can progressively digitalize
and formalize tacit manufacturing
knowledge as structured data. This is a key
pillar in building autonomous manufacturing
operations.
In tactical implementations, this structured
data can be used to give engineers
and operators contextualized alerts, helping
them react faster—and in smarter
ways—to improve operational performance.
It can also be used to start developing
a knowledge graph, making important
conceptual connections across the organization’s
data sets.
In strategic use cases, a digital twin’s
structured data enables real-time event-based
performance management. Systems
can start tailoring descriptive and predictive
insights and recommend the optimal
action in each situation (whether performed
by a human or a machine).
Ultimately, the goal is to create self-learning,
autonomous, closed loop systems
that can sense, interpret, and act by themselves.
In turn, freeing up engineers and
operators to focus on other critical activities.
These autonomous solutions can also
learn from the operation of the whole system
and continuously refine and improve
their actions within it.
Real-time data insights
provide a competitive edge
Embedding digital twins in supply chains
and manufacturing systems is a way for
manufacturers to contextualize their data
and start enabling some of the key next generation
use cases that undoubtedly
represent the future of manufacturing.
This is why all manufacturing organizations
should now be seriously considering digital
twin implementations as key enablers of
operational efficiency, cost optimization,
improved quality and customer satisfaction,
and competitive advantage.
