DTC HUB - Take Control of Your Stock - Flipbook - Page 19
EMERGING TECHNOLOGY
DIGITAL TWINS ARE
COMING TO THE
WORKSHOP FLOOR
The concept of a digital twin has
traditionally been associated with
large aerospace OEMs, complex
production lines and high-end
simulation environments. But
that is beginning to change. What
was once reserved for design and
engineering teams is starting to
move closer to the workshop floor,
into environments where processes
are physical, fast-moving and often
difficult to track in real time.
In practical terms, this shift is not about
creating a perfect virtual replica of an entire
facility. It is about building a clearer picture of
how work actually flows.
At its simplest, a digital twin is a virtual
representation of a physical process. It mirrors
what is happening in the real world, using data
to provide visibility, insight and, increasingly,
predictive capability. In manufacturing, this
has been used to model production lines, test
changes before implementation and optimise
performance at scale.
If rework increases, is it linked to a specific
stage, a specific material, or a change in
environmental conditions? If throughput
drops, is it due to process inefficiency, stock
availability or workflow disruption? These
are questions that are often answered
retrospectively, if at all. A digital twin brings
them into view as they happen.
What is different now is where that capability is
starting to appear.
For workshop environments, this creates a
different way of thinking about control. Rather
than relying solely on standard operating
procedures and individual experience, decisions
can be supported by data that reflects real
activity, not assumptions.
As data capture becomes easier and more
accessible, digital twins are no longer limited
to large, fixed production systems. They are
beginning to reflect smaller, more variable
operations, including maintenance, refit and
workshop environments. These are the types
of environments where processes are less
linear, where work is often bespoke, and where
visibility has traditionally relied on experience
rather than structured data.
In a paint environment, for example, a
digital twin could map the movement of a
component from preparation through to
topcoat, capturing time, material usage and
environmental conditions at each stage. It
allows teams to see where delays occur, where
variability is introduced, and where processes
deviate from the intended sequence.
The value is not just in observation. It is in
understanding cause and effect.
It also changes how improvements are
made. Instead of trial and error on live work,
adjustments can be modelled, tested and
understood before they are implemented.
This reduces risk, particularly in environments
where quality standards are high and the cost
of rework is high.
Importantly, adopting digital twin principles
does not require a full system overhaul. In
many cases, it begins with better visibility
of key variables. Material usage, stock levels,
environmental conditions and process timing all
contribute to a more complete picture
of operations.
As these data points become connected,
the foundation of a digital twin begins to
take shape.
This is where workshop environments start
to close the gap with larger manufacturing
operations. Not by replicating their scale, but
by adopting the same mindset. Understanding
processes as systems, rather than isolated tasks.
For industries where precision, consistency
and traceability matter, this shift has
clear implications.
It supports more predictable outcomes. It
reduces reliance on reactive decision-making. It
highlights inefficiencies that would otherwise
remain hidden.
Digital twins will not replace the skill and
judgement of experienced operators. But
they will change the context in which those
decisions are made.
As the technology becomes more accessible,
the question is no longer whether digital twins
belong in workshop environments. It is how
quickly those environments begin to benefit
from the visibility they provide.
In operations where performance is shaped by
detail, seeing the process clearly is often the
first step towards improving it.
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