What is a Digital Twin?
What is a Digital Twin?
Digital Twin is the
new buzzword in the world of digital transformation. Is it just a buzzword or
the next emergent technology? The simplest and meaningful explanation is that
digital twin is an executable digital representation of a physical object, product,
process or system across its lifecycle. One can make a virtual replica of
almost anything existing in the physical world like machines, buildings,
factories, cities and interestingly will soon have humans in the wider scope. The twin can take real-world data
as input and produce predictions based on simulation and analytics. Digital Twins is a concept, not a
standalone product. Multiple technologies like IOT, 3D simulation, block chain,
big data, edge computing, cloud computing and artificial intelligence are
involved to make the concept a reality.
How does it help?
replica of any physical objects helps to create various simulations to study
and analyze product performance on various parameters before a new launch or
product improvisation life cycle. Take it as a computer program for creating
simulations of physical objects to predict the performance challenges,
improvements, possible innovations and valuable data insights. The program has
capability to integrate artificial intelligence and software analytics for an
enhanced outcome. These learnings are then applied to the real object.
The journey of
digital twin is built by specialists, experts in data science or applied
mathematics. These developers research the dynamics of the physical object or
system and use that researched data to develop a virtual model that simulates
the real-world object in digital space. The twin model constructed is able to
receive input from sensors gathering data from a real-world counterpart. This
allows the twin to simulate the physical object in real time, in turn providing
valuable insights into potential product problems and performance challenges.
It is always not important to have the physical object real time. The twin
could also be designed based on a sample model. In such cases twin helps to
provide the initial feedback on how the actual product can be improvised. It is
like a pre-model before the actual product launch. The amount of data feed
taken into account determines how simple or complex the twin can be in
simulating a physical object.
Digital twin technology is becoming more widespread.
According to Deloitte study, the global market for digital twins is
expected to grow with 38% CAGR to reach $16 billion by 2023.
indicates that very soon humans will have a digital version of themselves. Ever
wonder how the virtual version of humans actually going to help? Well, the
digital version of our body will help keep a tab on our health status. One can
run series of simulations to diagnose potential health problems and preempt
them from actually happening. It is like the health check done not when the
problem has occurred but identify proactively through the digital tests and
work on controlling it from actually affecting the human body.
History of Digital Twin Technology
The term widely became a topic of discussion among
data scientists and IT professionals after Gartner one of the world leaders in
research and consultation named digital twins as one of its top 10
strategic technology trends for 2017 subsequently in 2018 and 2019 and hinting that the coming
years will see objects being represented by digital twins, a dynamic software
model of a physical thing or system". Digital twin is already
being used in real life applications in both B2C and B2B markets.
concept was first coined at NASA way back in 1960’s when they actually created
digital simulations of space capsules on- ground to study and test the issues
in orbit and design solutions.
In the year 1991, David Gelernter in his fascinating
book Mirror Worlds, first introduced
the concept. However, Dr. Michael Grieves, chief scientist for Advanced
Manufacturing gets the credit for actually applying digital twins to
manufacturing in 2002 and presenting his research on the digital twin software
concept. NASA’s John Vickers eventually introduced a new term “digital twin” in
his 2010 Roadmap Report.
What are the advantages of
Through digitization, there is ample data available
which is practically impossible to interpret and worked upon manually. Due to
inadequate resources and tools, rich data is being wasted with no conclusive
output. The digital twin is a computer program that integrates with artificial
intelligence, machine learning and various other technologies related to IIoT
(Industrial Internet of Things). The digital twin helps creating various
simulations of the product to provide valuable insights, fix product defects
and enhance improvisations. The various customer pain points can be studied
through digital twin technology and the cumulative insights can be presented as
a dashboard for the team to evaluate and act upon. Digital twins are changing
the way work is done in different industries with varying business
These virtual models have become fundamental in modern engineering
to drive various innovations and improved performance. Learning from the twin
has helped reduce failure costs in the real-world testing environment of
physical objects and provide data backed advanced analytical, predictive
capability to test processes and services. It is widely now used in large-scale products or projects
like healthcare, automotive, urban planning, construction, manufacturing,
aerospace, oil refinery management and retail industry.
To summarize, digital twin helps in
● Better R&D
● Product reengineering and recycling
● Greater efficiency
● Cost reduction
● Reducing equipment downtime
Digital Twin vs. Simulation
Are Digital Twins and simulations the same? The terms
simulation and digital twin are often used interchangeably, but there is a
fundamental difference. Simulation undoubted is an integral part of digital
twins, however the scope and use of digital twins is beyond simulations.
Simulations are typically used during the design phase of
a product's lifecycle, trying to showcase and forecast how a future product
will look and function. They may not have a direct equivalent of a real-world
object but just help understand what may happen in the real world. Digital twin
on the other hand is a replica/virtual model of a real-world object and is
built from the inputs from IoT sensors on real equipment and dynamically
changes with time. It is not just the predictable model of what may happen but
what is happening real time. So that makes it an integral part of the overall
product life cycle real-time - design, execute, change, decommission.
In these times when data is humongous, comprehending the
data for the accurate outcome is the trickiest and time-consuming task. Digital
twin real-time insights help prevent costly system tests and failures and
manage untimely maintenance issues. The decision-making ability becomes
concrete and result oriented.
Tesla uses digital twin for every car manufactured. Huge
data from the cars, are fed into the simulation models for real time analysis
and improvisations. Not too far away, will we see the car itself raise an alarm
for a checkup to the garage without the owner intervention.
Types of Digital Twins
four types of digital twins based on the complexity of what is being twinned. Abstraction and hierarchy are two
ways to manage the complexity of design.
Component or Parts twinning
Product or Asset twinning
System or Unit
core of the all the four types are the same i.e. they are the virtual replica
of a product or process to help predict and prevent potential damages and
suggest improvisations. The difference lies in the area of application
Part or component Twins - This is a twin of a single
component of the entire unit. They are the smallest example of a physical functioning component that
has a direct impact on performance and functionality.
Product or Asset
Twins - It is next higher level in digital
twin categorization. It is the simulation of two or more components working
together and their interactions are evaluated. They allow to explore the entire
System or Unit
Twins - This is a simulation of multiple
system assets and check their performance. The system twin can be used for
various types of applications.
Process Twins - It represents an overall production facility. It is a high-end
view of all systems working together and providing complete visibility of the
complex system or a factory overview. The process becomes successful only once
the parts, asset, system twin does their job as designed.
Overall, each level is a lower or higher
representation of digital twin but each one is equally important for a smooth
functioning and success of a machine.
Digital Twin on
At an industrial scale, digital twins have gained
momentum with the adoption of cloud and IOT devices. The digital twin requires
two-way connection between the physical asset and the virtual representation.
For most industries, the challenge with the digital twin is not the technology
but the deployment and investment in the huge infrastructure to support the
solution. While large enterprises could afford to experiment with the digital
twin the other small and medium enterprises struggle to use the digital twin
and take advantage of the learnings from this technology.
The affordable on demand cloud service providers are
the total game changers and removing the barrier. Cloud service providers are
offering digital twin related products, add data continuity and data safety as
the key functionality. Microsoft, for example, offers Azure Digital
Twins, described as “an Internet of Things (IoT) platform that enables one to
create a digital representation of real-world things, places, business
processes, and people.”
twin in a cloud-based environment can be remotely accessed by industry experts
in any part of the world and allows them to collaborate and contribute by
providing valuable data insights to the respective factory processes. One big
threat related to data security is well taken care on cloud. The cloud platform
layer security channels with 128- bit encryption and the risk of data theft is
retention is another big challenge in the huge industrial setups. Designing and
decoding of processes are dependent on skilled employees and any exits/ shuffle
would make the unlearn-relearn a time-consuming process. Cloud enables
integrated data continuity so knowledge retention is seamless in the event of a
loss of human or systems.
on the cloud gives SME’s an edge and humongous opportunity to optimize their
It is not just the buzzword but the next step in the
digital transformation journey. The Digital Twins’ adoption has just begun.
With its multiple advantages as highlighted earlier, it will have wide
acceptance across industries. It will play a vital role in research and
development space. Digital twins will enable optimized automation of processes,
which will result in efficiency gains. Data processing capabilities will be
improved significantly helping big and small organizations across various
domains increase productivity, improve performance, reduce operational costs
and understand the customer journey statistically.
If you want to know how Nebula Cloud is helping organizations
in building cloud based digital twin environments across various industries and
accelerating digital transformation, please write to email@example.com