- A digital twin has valuable roles in the design, build,
and operational phases of a product life cycle.
Later, I'll also briefly discuss the role
of digital twin beyond products.
Sometimes a digital twin will only exist
in either the design, build, or operate phases,
and sometimes it will provide value in all phases.
What's important, though, is that regardless of the phase,
a digital twin is a data-driven model.
Let's discuss this area.
In both the design and build phases,
data will inform essential decisions.
For example, environmental and historical data
can guide design choices.
In fact, repeated virtual simulations
under different conditions are used
to determine all manner of construction decisions.
Variable conditions can include the materials used,
weather, natural disaster preparedness,
particular mechanical parts choices,
and indoor environmental factors such as humidity and heat.
High-quality data at volume can result
in much higher performing end products.
While the digital twin concept offers significant advantages
in the design and build phases of a product life cycle,
an important focus of a digital twin is its benefits
during the operations and maintenance of a physical object.
I'll focus here next.
A digital twin is a data-driven model
of a real-world counterpart.
The digital twin is fed real-time data
from censors placed on the physical object.
The object can range from a complex machine
on an assembly line to a wind turbine,
mineral extraction machinery, a building, a city,
healthcare devices, and a human, and way beyond.
We'll look at some examples in detail later.
While a digital twin consumes and produces data output,
it can also feed data into the physical object too.
In this regard, the pairing of the digital and physical
supports a bidirectional data flow relationship.
This is called virtual twinning.
In the world of digital twins, data is king.
By understanding the real-time data produced
by the physical object, we can analyze its current state,
its behavior over time, and even predict future scenarios.
This can enable more efficient monitoring that helps,
for example, with identifying potential problems.
The real-time data also supports the implementation
of system improvements,
and it opens up options for adding new value.
Critical to what is measured
and how precise it is depends on the degree
to which the real-world counterpart is fitted with sensors
and the number of data points that are collected.
Here, the more of both, the better.
Determining the number of variables
to measure is based entirely on each use.
For example, a wind turbine will have a different sensor
and data collection configuration
than a Boeing 787 Dreamliner.
A digital twin might collect a few data points or thousands.
Examples of basic data collected during operations
might include vibration, position of parts, temperature,
pressure, and mechanical stresses.
Data collected from a physical object
can be rendered visually on the digital replica such
that the data shown maps directly to specific parts.
However, this is not always the case.
A digital twin can render data
in a number of different types of graphs.
For those interpreting the data,
an identical physical image may not be useful.
The rendering of data in a digital twin
will reflect the specific vendor solution
and the data lens that adds the most value.
Of course, data can be extracted and analyzed
through any number of data analytics platforms.
This can be done in real time,
or specific content can be exported
for later analysis of historical data.
Finally, it quickly becomes clear that data science
in this field is a highly desired skill.
Those who focus in this area can likely expect
to find an increasing number of opportunities.