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without incurring the prohibitive costs of offer the lowest possible air resistance?
developing physical prototypes.
Let us set the context by defining the
concept of digital twin. For starters,
Digital twin means an integrated multi-
physics, multi-system simulation of a
complex product, which functions to
mirror the life of its corresponding twin.
Digital twin consists of three parts- (i) the
virtually defined product including its
subsystems (ii) the physical product and
(iii) the links between physical and virtual
product. It serves as a bridge between the
physical world and the digital world.
The digital twin is characterised by the
two-way interactions between the digital
and physical worlds, which may result in
many benefits. In the physical world, the
real product can be made smarter to
actively adjust its real-time behaviour
according to the recommendations made
by the virtual product or the twin.
Whereas in the virtual world, the digital
version of the product can be made more
realistic to accurately reflect the real-
world state of the physical product.
In process-driven functions, digital twins
constantly receive data feeds from
interconnected machines, helping in
predictive maintenance and running the
business as usual without downtime.
Gartner estimates that by 2021 - just a
couple of years from now -- half of all
major industrial companies will be using
digital twins. Key Industry verticals will
be a key beneficiary of this.
With a new model being launched on an
average every six months or so, a digital
twin of the model serves as a test bed,
helping manufacturers to design,
customise and test every part while
running it in a virtual environment, saving
crucial time and millions on developing
physical prototypes. Engineers can
simulate and validate product properties,
for example - Is the product stable, and is
it intuitive to use? Does the car bodywork
Do the electronics operate reliably?
For Ford Motors, Siemens' Digital
Enterprise Suite operates across its value
chain. It enabled Ford to increase their
number of models, reduce factory energy
cost by up to 30% and development time
by up to 40%.
The same advantages are seen even in the
heavy machinery industry. Let's say, for
example, a manufacturer needs to develop
a steam turbine. Traditionally, engineers
would take several months to design the
product, physically build it and then test it
for performance. On the other hand, by
using a digital twin, they can test and
evaluate all parameters with high
precision -- ranging from material to
performance, while obtaining valuable
data that would help make the final set of
changes before the actual turbine is
developed.
NASA's Mars Science Laboratory mission
- which includes the latest rover,
popularly known as Curiosity - is the most
technologically complex project in the
space agency's history. It is an example of
the seamless communication that is
developing among machines - the meeting
of the real and virtual worlds.
In process industries, digitalisation
enables companies to build a
comprehensive data model, ensuring
shorter time to market, greater flexibility,
and increased efficiency, while allowing
companies to respond successfully to the
volatility and diversity of global markets,
thereby giving them a lasting competitive
edge.
The backdrop to the digital twin practice
is digitalisation, which offers immense
possibilities in redefining the
'productivity' criteria as businesses get
access to high-quality data through digital
real-time supply chain, digital product
definition and digitally crafted and run