What is a Digital Twin?

A Digital Twin is a virtual model of a physical system that is used to simulate

and

to predict its behavior in real-time.

Also Digital Twin is a virtual model of a physical process that is used to simulate

and

to predict its behavior in real-time.

Hence it is essentially a digital replica of a physical asset or system

that can be used to monitor its performance,

or it can be used to analyse its behavior,

as well as it can be used to optimize its operation.

Also Digital Twins are created using a combination of real-time data

from sensors,

from machine learning algorithms,

as well as from computer-aided design (CAD) software.

Further the Digital Twin can be used to simulate different scenarios, such as

it can be used to test new designs,

as well as it can be used to predict the impact of changes

or it can be used to predict failures in the physical system.

In particular Digital Twins are increasingly used in industries such as

manufacturing /

construction /

and energy

to improve efficiency,

to reduce costs,

and

to enhance safety.

For example, a Digital Twin of a manufacturing plant can be used

to optimize production processes

as well as it can be used to reduce downtime.

While a Digital Twin of a building can be used

to monitor energy consumption

and

to optimize HVAC systems.

Overall Digital Twins offer a powerful tool for improving the performance

and

reliability of physical systems,

while also reducing costs

as well as minimizing risk.

Research findings from an analysis of 100 Digital Twins projects

In short

The market for Digital Twins expanded by 71% between 2020 and 2022.

63% of manufacturers are currently developing a Digital Twin

or have plans to develop a Digital Twin.

The latest Digital Twin Market Report 2023–2027 highlights the six main Digital

Twin applications today:

-system prediction,

-system simulation,

-asset interoperability,

-maintenance,

-system visualization, and

-product simulation.

Why does it matter?

Although many Digital Transformation initiatives involve the creation of digital

replicas of physical assets (Digital Twins).

But understanding the nuances of different Digital Twins and activity hotspots

helps vendors to focus their efforts

and

helps end users to pick the most value-generating initiatives.

Digital Twins have become a key topic for Industry 4.0

The market for standalone Digital Twin software grew 71% between 2020

and

2022,

according to the recently published 

Digital Twin Market Report 2023–2027.

Survey results, also part of this research, indicate strong adoption.

For example, 29% of manufacturing companies globally have fully implemented

or are implementing a Digital Twin strategy for a portion of their operational

assets.

As well as further 63% are currently developing or have already developed their

Digital Twin strategy.

Although the roots of Digital Twins go back to NASA’s Apollo program in 1970.

But the concept of creating digital replicas of physical assets and

visualizing /

simulating /

predicting 

in a virtual world is extremely suitable for companies.

If  they are trying to make Industry 4.0 a reality

or

they are aiming toward future industrial metaverse projects.

Then please make no mistake.

While the definition of a Digital Twin may be straightforward,

yet its applications are numerous.

In fact in 2020, IoT Analytics published their first market research on the topic

as well as showcased 

that there are 200

or

more different types of Digital Twins.

Though they received feedback from the market was that classification helps to

ensure apple-to-apple Digital Twin comparisons.

Yet questions remain about the hotspots of activity.

Therefore, as part of IoT Analytics’ new 233-page Digital Twin Market Report 2023-2027,

they classified 100 real Digital Twin projects along the three dimensions

and found six main areas of activity.

These six Digital Twin application hotspots cover two thirds of all Digital Twin

projects which they analysed.

Classifying Digital Twins

In fact IoT Analytics defines a Digital Twin as a virtual model replicating the

behavior of an existing

or

a potential real-world asset,

system,

or

multiple systems.

In fact we can describe the concept of Digital Twins in three main dimensions. 

Each axis of the cuboid represents one dimension of the Digital Twin:

• Life cycle phase: The X-axis represents the six life cycle phases a Digital Twin is used for, from design / build / operate / maintain / optimize to decommission.
• Hierarchical levels: The Y-axis represents the five hierarchical levels a Digital Twin represents, from information / component / product / system to multi-system.
• Use / purpose of implementation: The Z-axis represents the seven most common uses for Digital Twins, such as Digitize / Visualize / Simulate / Emulate / Extract / Orchestrate  and Predict.

Hence there are 210 potential different Digital Twin combinations

(5 x 6 x 7 = 210),

while research of IoT Analytics indicates that many Digital Twin initiatives

cater to more than one combination.

The six most common Digital Twin applications

As part of the research, IoT Analytics looked at 100 Digital Twin case studies

and classified each project into the Digital Twin cuboid.

The result was that six clusters of Digital Twin activity stand out.

IoT Analytics call them Digital Twin applications:

Digital Twin application 1: System prediction

Further we consider 30% of the analysed Digital Twin projects as Digital Twins

for system prediction.

Therefore we use the Digital Twins for predicting entire systems

(such as a part or the entirety of a factory /  building / wind farm or city)

during its

“operate”

or

“optimize” life cycle phase.

Such Digital Twins focus on predicting the behavior

and

future state of a physical system using current data

and

relevant records of the operational history.

At the core of predictive Digital Twins are predictive models

that we use to predict future outcomes.

System prediction Digital Twin project example:

Korean wind farm operator Doosan Enerbility deployed a Digital Twin to help

predict power output,

thus aiding the operations of their wind farms.

In this case the Digital Twins incorporate data from IoT sensors on wind turbines

along with weather data

and

a model of expected power output.

The Digital Twin serves two main purposes:

a.) Digital Twin is acting as a performance watchdog,

where a physics-based model of the wind turbines calculates theoretical output,

by taking into consideration the current weather conditions

and comparing the theoretical output with the actual output

in order to flag out any anomalies.

b.) Digital Twin is acting as an output prediction tool

for improved planning

that predicts future of power generation 

based on forecasted weather data.

Finally the Digital Twin-based power output predictions

assisted Doosan

in improving wind farm revenues

by increasing the output commitments

which the company made to South Korean energy grid operators

while avoiding fines

for failing to meet those commitments.

For more information,

see Doosan Wind Farm Digital Twin: Visualizing IoT and Machine Learning

Digital Twin application 2: System simulation

Further we consider 28% of the analysed digital twin projects as digital twins for

system simulation,

where we simulate a system either during its “build,”

“operate,”

or

“optimize” life cycle phases.

For instance examples include factory simulations prior to opening up

or

making significant changes 

in rail network simulation / 

traffic simulation.

Thus one of the key benefits of such system simulations is to reduce costs

by testing the types of assets used /

key operational parameters

and other important system variables prior to making the changes.

System simulation Digital Twin project example:

For instance train manufacturer Siemens Mobility needed to provide 170 new

high-speed trains to German train operator Deutsche Bahn AG.

For these trains, Siemens developed a new car-based train control architecture

that had not been deployed before.

Therefore to reduce development costs, Siemens developed a digital twin

that would simulate the entire train’s functionality of 40 different subsystems.

The main feature of the solution was a functional simulation

that used a representation of the electrical schema of the train

in the simulated environment.

Siemens estimated that the Digital Twin-based simulation reduced cost

by $1 million to $8 million.

For more information,

see Reducing Risk and Cost with Virtual High-Speed and Commuter Train Test

Digital Twin application 3: Asset interoperability

Also we consider 24% of the analysed Digital Twin projects as Digital Twins for

asset interoperability,

where Digital Twins streamline common data formats and allow for standardized

data in / data output during the  “operate” or

“optimize” life cycle phase.

In any case Asset interoperability twins allow for real-time extraction of data from

assets along various dimensions, including asset features / characteristics / 

properties / statuses / parameters / 

measurement data /

and capabilities.

Although asset interoperability is an underlying goal of any digital twin project.

Yet the main idea when implementing a specific asset interoperability twin is

to develop a standardized way to address assets

as well as

the ability to promptly integrate new assets to the overall system.

Moreover many of the specific asset interoperability twin initiatives

align on interoperability frameworks provided

by major industry consortia,

such as the Digital Twin Consortium’s Interoperability framework 

or

Platform Industrie 4.0 / IDTA’s Asset Administration Shell.

Asset interoperability Digital Twin project example:

For instance Formula One team Scuderia Ferrari built a Digital Twin

to help integrate

and 

analyse the various sensor data points

coming from the vehicle

and

allowing diverse teams

to collaborate on analyzing aerodynamics /

power /

vehicle dynamics / and

race engineering.

In either case the ability to have all data sources streamlined  into a Digital Twin

helped the team make faster 

and

more data-driven decisions.

For more information,

see Palantir Technologies + Scuderia Ferrari Partnership Overview

Digital Twin application 4: Maintenance

Further we consider 21% of the analysed Digital Twin projects as Digital Twins 

for  maintenance, 

where the main purpose of the Digital Twin is to assist a system during the

“maintain” phase of the life cycle, 

often involving some form of “prediction.”

For example Digital Twins catering to the maintain life cycle phase are mostly

geared toward assuring a system’s operational effectiveness, e.g.

by assisting maintenance personnel during scheduled downtime / repair tasks

by providing them with in-depth information about the physical asset /

system.

In fact Digital Twins for Maintenance enable predictive maintenance use cases

that aim to prevent asset failures altogether,

thereby avoiding costly downtime.

Maintenance Digital Twin project example:

As a result German utility company E.ON embarked on a project that allowed the

company to shift to a more preventive /

predictive / and

risk-based method of maintaining its assets.

As part of this project, E.ON implemented a cloud-based Digital Twin

that they use for better prediction of asset failures.

In particular the Digital Twin allowed the company to evaluate failure modes

per asset type and determine the remaining life of the equipment fleet.

For more information,

see DNV Creates Digital Twin for E.ON

Digital Twin application 5: System visualization

Further 20% of the analysed Digital Twin projects are considered Digital Twins

for system visualization,

where the Digital Twin is used to visualize a system during its  “operate” life cycle

phase.

Among the most common types of visualizations are 3D visual elements by

using CAD drawings.

For example it helped to create better transparency about the current operating

conditions of the system.

System visualization Digital Twin project example:

In fact the national railway operator of Italy, Ferrovie dello Stato, built

a Digital  Twin of the rail infrastructure. 

It included more than 10,000 miles of tracks /

stations / 

tunnels /

bridges /

signals /

switches /

electrification hardware /

and

IT systems 

that coordinate everything.

Further the digital twin sourced the data from sensors such as cameras / 

GPS  receivers /

and

employed advanced learning algorithms to create an interactive

3D replica of the rail system. 

ArcGIS was used for 3D visualization of geographical elements.

It created a complete visual inventory of the railway infrastructure.

Later the resulting Digital Twin helped railway managers 

in the control room to visualize infrastructure

such as bridges 

and

stations remotely throughout the rail network.

In addition it also helped the local staff to prevent safety hazards

as well as

to boost on-time performance.

For more information, 

see How Digital Twin Technology Is Helping Build a Smart Railway System in Italy

Digital Twin application 6: Product simulation

 

Further we consider 9% of the analysed Digital Twin projects as Digital Twins

for product simulation, 

where we simulate a future product during the “design” /

or 

“build” life cycle phase.

Digital Twins play a key role for developing new and improved products.

Thus the key use case is to simulate different designs of a potential future product

prior to building it.

Hence thereby eliminating the need to build costly prototypes /

as well as

allowing the need for quick testing of thousands of product variances.

Product simulation of Digital Twins often reside in / or closely connected to

computer-aided engineering (CAE) software 

as well as

sometimes computer-aided design (CAD) software.

In short typical types of simulations include fluid dynamics / 

mechanical performance / 

or 

compatibility of electromagnetic.

Product simulation Digital Twin project example:

For instance German packaging and bottling machine manufacturer Krones used

a Digital Twin to test new product designs for one of its automated beverage-

packaging system.

The company wanted to incorporate a dynamic tripod robot into the product

design.

Therefore to do so, the friction and force of gravity on sliding packages across a

moving conveyor belt as well as many other similar factors had to be tested.

Hence Krones developed a model for the tripod robot

by importing geometry

and 

inertia data from STEP files exported from CAD software.

By modelling and simulating the tripod robot,

Krones was able to increase the performance efficiency of the robot.

Product development time was shortened.

As well as testing time was significantly reduced.

For more information,

see Krones Develops Package-Handling Robot Digital Twin.

Conclusion

To summarize this research shows that there is strong interest in Digital Twins.

It must be noted that all Digital Twins are not equal.

In fact every Digital Twin project is distinct in terms of its sophistication.

Also every Digital Twin project is distinct in terms of its life cycle phase to which it

caters.

As well as every Digital Twin project is distinct in terms of its use.

It is important to realize that there is not one best or one single use for Digital

Twins.

Basically it really depends on the end goal.

For some Digital Twin applications,

simulation may be the key use (e.g., when  wanting to test “what-if” scenarios);

for others,

emulation may be the key use (e.g., when checking for configuration errors or

when training operators);

and for others still,

prediction is the key use (e.g., when estimating future performance).

As a result IoT Analytics identified six clusters of Digital Twin applications

that reflect two-thirds of all Digital Twin projects today.

With regard to the most common use,

31% of the Digital Twin projects are focused on prediction at the system level.

What it means to companies looking to adopt Digital Twins?

Above all it is important for organizations to look at Digital Twins as a means to

achieve broader Digital Transformation.

Although companies often start small

however many end up scaling

as well as

may end up integrating 

Digital Twin initiatives into larger digital threads.

Companies should consider questions such as:

1. What problems should our Digital Twin solve?
2. Do we need more than one Digital Twin project?
3. Should we look at the Digital Twin with the broader mind-set of all 200+potential combinations?

IoT Analytics’ research also indicates that 72% of the organizations

that embarked on a Digital Twin project reported facing a challenge

with the cost of building Digital Twin solutions

despite the success of Digital Twins,

As with any uncertain technology initiative, adopters should also assess their

readiness to adopt Digital Twins

in terms of the existing technology stack

as well as  

in terms of the existing in-house talent.

IoT Analytics’ research highlights that a number of “legacy” software tools such as

CAD /

CAE / and

PLM /

as well as new tools such as IoT Platforms /

and

the cloud

play an important role when developing Digital Twins.

User of Digital Twin should quantify the benefits of the Digital Twin,

and compare it with the opportunity cost of not implementing a Digital Twin for

the respective use case / proposed application.

Certain Digital Twin projects take years before they materialize

because ontology Mapping /

creating complex visualizations /

or

developing suitable prediction algorithms sometimes take longer than

anticipated.

 

What it means to companies in the Digital Twin vendor ecosystem?

In fact Digital Twin vendors currently enjoy favorable market conditions

being Digital Twins one of the highest growth areas in enterprise software

right now.

However vendors may want to consider the following three aspects:

1. They may need six different Digital Twin go-to-market strategies.

In short the six Digital Twin application areas suggested by IoT Analytics

as well as highlighted in this article are distinctly different.

To this end application areas such as system prediction

as well as

system simulation are hot-spots right now.

In fact more customers are ready to adopt them.

However, as a vendor, are you addressing each cluster correctly in your go-to-

market strategy?

Please think over.

   2. Partnerships are important. 

Because vendors need to keep an eye on fine-tuning the existing Digital Twin 

offerings by amalgamating them with new technologies

as well as with new partners.

As a result this enables them to offer the entire end-to-end solution

that no vendor today covers alone.

For example, 

Digital Twin vendor Siemens teamed up with Nvidia in a partnership 

that supports both companies’ ecosystems.

   3. Digital twins are not always based on standalone solutions. 

Many Digital Twin solutions are integrated within the larger software landscape

such as CAD /

CAE /

and

simulation.

Although  the reference architectures of IoT Analytics were analysed as part of the

research.

As well as it highlights the need for Digital Twin-specific software.

Also it can reside within a different or within a broader software offering.

 

Are you interested in learning more about Digital Twin applications?

In fact  IoT Analytics is a leading global provider of market insights

as well as

it is a strategic business intelligence for

the Internet of Things (IoT) / 

AI /

Cloud /

Edge /

and  Industry 4.0.

                                            By Digital Prabhat

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