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A DHL perspective on the impact of
digital twins on the logistics industry
DHL Trend Research
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Digital Twins in
Logistics
Contents
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Preface
1 Understanding Digital Twins 4
1.1 The Digital Twin Comes of Age 4
1.2 What Makes a Digital Twin? 6
1.3 Underlying Technologies Enabling Digital Twins 7
1.4 How Digital Twins Create Value 8
1.5 The Digital Twin Through the Product Lifecycle 9
1.6 Challenges in Applying Digital Twins 10
2 Digital Twins Across Industries 12
2.1 Digital Twins in Manufacturing 13
2.2 Digital Twins in Materials Science 14
2.3 Digital Twins in Industrial Products 15
2.4 Digital Twins in Life Sciences and Healthcare 16
2.5 Digital Twins in Infrastructure and Urban Planning 17
2.6 Digital Twins in the Energy Sector 19
2.7 Digital Twins in Consumer, Retail and E-commerce 20
3 Digital Twins in Logistics 21
3.1 Packaging & Container Digital Twins 22
3.2 Digital Twins of Shipments 23
3.3 Digital Twins of Warehouses and Distribution Centers 23
3.4 Digital Twins of Logistics Infrastructure 26
3.5 Digital Twins of Global Logistics Networks 27
4 Logistics Implications of Implementing Digital Twins 28
4.1 Inbound to Manufacturing 29
4.2 In-plant Logistics 30
4.3 Aftermarket Logistics 32
4.4 Orchestrating the Supply Chain 32
Conclusion & Outlook 34
Sources 36
Pictorial Sources 37
Further Information 38
Recommended Reading 39
For centuries, people have used pictures
and models to help them tackle complex
problems. Great buildings first took shape
on the architect’s drawing board. Classic
cars were shaped in wood and clay.
Over time, our modeling capabilities have
become more sophisticated. Computers
have replaced pencils. 3D computer
models have replaced 2D drawings.
Advanced modeling systems can simulate
the operation and behavior of a product
as well as its geometry.
Until recently, however, there remained
an unbridged divide between model and
reality. No two manufactured objects
are ever truly identical, even if they have
been built from the same set of drawings.
Computer models of machines don’t
evolve as parts wear out and are replaced,
as fatigue accumulates in structures, or as
owners make modifications to suit their
changing needs.
That gap is now starting to close. Fueled
by developments in the internet of things
(IoT), big data, artificial intelligence,
cloud computing, and digital reality
technologies, the recent arrival of digital
twins heralds a tipping point where
the physical and digital worlds can be
managed as one, and we can interact
with the digital counterpart of physical
things much like we would the things
themselves, even in 3D space around us.
Led by the engineering, manufacturing,
automotive, and energy industries in
particular, digital twins are already
creating new value. They are helping
companies to design, visualize, monitor,
manage, and maintain their assets more
effectively. And they are unlocking new
business opportunities like the provision
of advanced services and the generation
of valuable insight from operational data.
As logistics professionals, we have been
thinking about how digital twins will
change traditional supply chains, and how
the logistics sector might embrace digital
twins to improve its own processes. Our
objective in writing this report is to share
our findings and to help you answer the
following key questions:
■ What is a digital twin and what does
it mean for my organization?
■ What best-practice examples from
other industries can be applied to
logistics?
■ How will my supply chain change
because of digital twins?
Looking ahead, we believe that the
adoption of digital twins across industries
will drive better decision making in the
physical world. That, in turn, will drive
significant changes in the operation of
supply chains and logistics processes.
In the logistics industry itself, digital
twins will extend the benefits of IoT
already being applied today. They will
bring deeper insight into the planning,
design, operation, and optimization of
supply chains, from individual assets
and shipments to entire global supply
networks.
We think there has never been a
more exciting time for industries and
logisticians to work together to leverage
the full potential of digital twins. On
behalf of us all at DHL, we look forward to
collaborating with you in this exciting and
potentially transformative field.
Preface
Matthias Heutger
Senior Vice President
Global Head of Innovation & Commercial
Development, DHL
Markus Kueckelhaus
Vice President
Innovation & Trend Research, DHL
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Chapter 1
Understanding Digital Twins
1.1 THE DIGITAL TWIN COMES OF AGE
For many years, scientists and engineers have created
mathematical models of real-world objects and over time
these models have become increasingly sophisticated. Today
the evolution of sensors and network technologies enables
us to link previously offline physical assets to digital models.
In this way, changes experienced by the physical object are
reflected in the digital model, and insights derived from the
model allow decisions to be made about the physical object,
which can also be controlled with unprecedented precision.
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1960 1985 2000 2015
The evolution of digital twins
2017
Gartner lists digital twins as
a top 10 tech trend
2018
Digital twins in product
portfolios of all major software
and industrial companies
2002
• Dr. Grieves‘ concept of a
digital twin emerges
• McLaren F1 digital twin
technology for product development and performance prediction
2011
NASA & USAF
papers on
digital twins
2015
GE digital wind farm
initiative
1983 - 2001
AutoCAD becomes a de facto
tool in nearly all engineering
and design
1982
AutoCAD is born
1977
Flight simulators with
computer simulation
1970
NASA pairing technology
on Apollo 13 mission
Simulation tools drop in price,
broadening availability and
applicability to many engineering
and design fields.
Advanced simulation becomes
central to complex, multidisciplinary system design and
engineering. An enhanced range
of simulation applications enables
model-based systems
engineering.
A virtual model (once only used
in simulation) is seamlessly and
continually updated across the
entire lifecycle of a product,
where the virtual model supports
operation of the physical product
through direct linkage and
representation of its operational
data.
Simulation emerges in specific
and highly specialized fields for
expert use only.
Computer-driven
Simulation
Simulation
Applications
Simulation-driven
System Design
Digital
Twins
While the digital twin concept has existed
since the start of the 21st century, the
approach is now reaching a tipping point
where widespread adoption is likely in the
near future. That’s because a number of
key enabling technologies have reached
the level of maturity necessary to support
the use of digital twins for enterprise
applications. Those technologies include
low-cost data storage and computing
power, the availability of robust, highspeed wired and wireless networks, and
cheap, reliable sensors.
The use of a high-fidelity simulation or
a direct physical replica to support the
operation and maintenance of an asset
has a long history. NASA pioneered a
pairing approach during the early years
of space exploration. When the Apollo 13
spacecraft suffered significant damage
on a mission to the moon in 1970,
NASA engineers were able to test and
refine potential recovery strategies in a
paired module on earth before issuing
instructions to the stricken crew. To this
day, pairing - now using digital models
- remains a central part of the US space
agency’s strategy for managing space
missions.
At first the complexity and cost involved
in building digital twins limited their
use to the aerospace and defense
sectors (see the timeline in figure 1) as
the physical objects were high-value,
mission-critical assets operating in
challenging environments that could
benefit from simulation. Relatively few
other applications shared the same
combination of high-value assets and
inaccessible operating conditions to
justify the investment.
That situation is changing rapidly.
Today, as part of their normal business
processes, companies are using their
own products to generate much of the
data required to build a digital twin;
computer-aided design (CAD) and
simulation tools are commonly used in
product development, for example. Many
products, including consumer electronics,
automobiles, and even household
appliances now include sensors and data
communication capabilities as standard
features. Figure 1
Figure 2
Figure 1: The evolution of digital twins. Source: DHL
Figure 2: GE has created a digital twin of the Boeing
777 engine specifically for engine blade maintenance.
Source: GE
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Attributes of a digital twin
A digital twin is a
virtual representation of
a physical asset
Continuously collects
data (through sensors)
Associated with a single,
specific instance of a
physical asset
Continuously connected to the
physical asset, updating itself with
any change to the asset‘s state,
condition, or context
Represents a unique
physical asset
Provides value through visualization,
analysis, prediction, or optimization
Figure 3
As corporate interest in digital twins
grows, so too does the number of
technology providers to supply this
demand. Industry researchers expect
the digital twins market to grow at an
annual rate of more than 38 percent over
the next few years, passing the USD $26
billion point by 2025.
Plenty of technology players have an eye
on this potentially lucrative space. The
broad range of underlying technologies
required by digital twins encourages
many companies to enter the market,
including large enterprise technology
companies such as SAP, Microsoft,
and IBM. These organizations are well
positioned to apply their cloud computing,
artificial intelligence, and enterprise
security capabilities to the creation of
digital twin solutions. In addition, makers
of automation systems and industrial
equipment such as GE, Siemens, and
Honeywell are ushering in a new era of
industrial machinery and services built
on digital twins. Also companies offering
product lifecycle management (PLM)
such as PTC and Dassault Systèmes are
embracing digital twins as a fundamental
core technology to manage product
development from initial concept to end
of life. Digital twin opportunities are also
attracting the attention of start-ups, with
players such as Cityzenith, NavVis, and
SWIM.AI developing their own offerings
tailored to particular niches and use
cases.
1.2 WHAT MAKES A
DIGITAL TWIN?
In practice with so many different
applications and stakeholders involved,
there is no perfect consensus on what
constitutes a digital twin. As our examples
show very clearly later in this report, digital
twins come in many forms with many
different attributes. It can be tempting for
companies to ride the wave of interest
in the approach by attaching a ‘digital
twin label’ to a range of pre-existing 3D
modeling, simulation, and asset-tracking
technologies. But this short sells the
complexity of a true digital twin.
Most commentators agree on key
characteristics shared by the majority of
digital twins. The attributes that help to
differentiate true digital twins from other
types of computer model or simulation are:
■ A digital twin is virtual model of a real
‘thing’.
■ A digital twin simulates both the
physical state and behaviour of the
thing.
■ A digital twin is unique, associated
with a single, specific instance of the
thing.
■ A digital twin is connected to the thing,
updating itself in response to known
changes to the thing’s state, condition,
or context.
■ A digital twin provides value through
visualization, analysis, prediction, or
optimization.
The range of potential digital twin
applications means that even these defining
attributes can blur in some situations. A
digital twin may exist before its physical
counterpart is made, for example, and
persist long after the thing has reached the
end of its life. A single thing can have more
than one twin, with different models built
for different users and use cases, such as
what-if scenario planning or predicting the
behavior of the thing under future operating
conditions. For example, the owners of
factories, hospitals, and offices may create
multiple models of an existing facility as
they evaluate the impact of changes in
layout or operating processes.
Figure 3: Characteristics of a digital twin. Source: DHL
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Renders the spatial model and
visualization of the digital twin,
providing the medium for collaboration and interaction with it.
Virtual Reality
Augmented, Mixed &
Provide the necessary tools to
extract, share, and harmonize
data from multiple systems
that contribute to a single
digital twin.
Standards
APIs and Open
High-precision sensors enable
continuous collection of
machine data, state, and condition from the physical asset
to its digital twin in real time
via wireless networks.
Internet of Things
Leverages historical and real-time
data paired with machine learning
frameworks to make predictions
about future scenarios or events
that will occur within the context
of the asset.
Artificial Intelligence
Allows storage and processing of large volumes
of machine data from the
asset and its digital twin in
real time.
Cloud Computing
Underlying technologies
of digital twins
Today, researchers and technology
companies have built digital twins at
every scale from atoms to planets. The
smallest digital twin can represent the
behavior of specific materials, chemical
reactions, or drug interactions. At the
other extreme, a large digital twin can
model entire metropolitan cities. The
majority of digital twins sit somewhere
in the middle, with most current
applications aimed at more humanscale problems, especially the modeling
of products and their manufacturing
processes. One notable trend is the
development of larger, more complex
digital twins as organizations evolve from
modeling single products or machines
to modeling complete production lines,
factories, and facilities. Similarly, efforts
are underway to create digital twins of
entire cities or even of national-scale
energy infrastructure and transport
networks. The UK is even working on
plans to develop a digital twin of the
whole country to serve as a repository
for multiple sources of data related to
buildings, infrastructure, and utilities.
1.3 UNDERLYING
TECHNOLOGIES
ENABLING DIGITAL
TWINS
Five technology trends are developing in
a complementary way to enable digital
twins, namely the internet of things, cloud
computing, APIs and open standards,
artificial intelligence, and digital reality
technologies.
The Internet of Things (IoT). The rapid
growth of IoT is one important factor
driving the adoption of digital twins.
IoT technologies make digital twins
possible because it is now technically
and economically feasible to collect large
volumes of data from a wider range of
objects than before. Companies often
underestimate the complexity and volume
of data generated by IoT products and
platforms, requiring tools to help them
manage and make sense of all the data
they are now collecting. A digital twin is
often an ideal way to structure, access, and
analyze complex product-related data.
Digital twins rely on a host of underlying
technologies that are only now reaching
the point where they can be applied
reliably, cost effectively, and at scale.
Cloud Computing. Developing,
maintaining, and using digital twins
is a compute- and storage-intensive
endeavor. Thanks to the continually falling
cost of processing power and storage,
large data center networks with access
provided via software-as-a-service (SaaS)
solutions now enable companies to
acquire exactly the computing resources
they need, when they need them, while
keeping costs under control.
APIs & Open Standards. Closed,
proprietary-by-design simulation tools
and factory automation platforms are
increasingly becoming a thing of the
past. Technology companies created
and protected their own data models,
requiring intensive, ground-up software
development to build infrastructure from
scratch for each new product.
Figure 4: Technologies behind digital twins. Source: DHL
Figure 4
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Now the availability of open standards and
public application programming interfaces
(APIs) has dramatically streamlined
sharing and data exchange, making it
possible for users to combine data from
multiple systems and tools quickly and
reliably.
Artificial Intelligence (AI). Dramatic
improvements in the power and usability
of advanced analytical tools have
transformed the way companies extract
useful insights from big, complex data
sets. Machine learning frameworks are
enabling the development of systems that
can make decisions autonomously as well
as predictions about future conditions
based on historical and real-time data.
Augmented, Mixed, and Virtual Reality. In
order to leverage, consume, and effectively
take action on the insights generated by
a digital twin, it must be rendered either
on a screen (2D) or in physical space (3D).
To date, most digital twins have been
rendered in two-dimensional space, as the
conventional computing norms of today
limit us to displays on monitors, laptops,
and other screens. But increasingly,
augmented reality is enabling us to
display digital content in 3D. In addition,
mixed reality allows us to interact with
digital content in our existing physical
environment. And virtual reality allows
us to create entirely new environments to
render digital twins in a highly immersive
way, creating the richest consumption of
and interaction with the information.
While the above technologies – IoT, cloud
computing, APIs, and artificial intelligence
– provide the underlying sensing and
processing infrastructure required to
create a digital twin, augmented, mixed,
and virtual reality are the tools for
visualizing digital twins and making them
real to the user.
1.4 HOW DIGITAL
TWINS CREATE VALUE
Digital twins can be used in different ways
to add value to a product, process, user,
or organization. The value available, and
the investment required to capture it, are
highly application dependent. Most fall
into one or more of the following broad
categories.
Descriptive Value. The ability to
immediately visualize the status of an
asset via its digital twin is valuable when
those assets are remote or dangerous
– examples include spacecraft, offshore
wind turbines, power stations, and
manufacturer-owned machines operating
in customer plants. Digital twins make
information more accessible and easier to
interpret from a distance.
Analytical Value. Digital twins that
incorporate simulation technologies
can provide data that is impossible to
measure directly on the physical object
– for example information generated
inside an object. This can be used as a
troubleshooting tool for existing products
and can help to optimize the performance
of subsequent product generations.
Diagnostic Value. Digital twins can
include diagnostic systems that use
measured or derived data to suggest the
most probable root causes of specific
states or behaviors. These systems can
be implemented in the form of explicit
rules based on company know-how,
or they may leverage analytics and
machine learning approaches to derive
relationships based on historical data.
Predictive Value. The likely future state
of the physical model can be predicted
using a digital twin model. One example
is GE’s use of digital twins in wind farms
to predict power output, as depicted in
figure 5. The most sophisticated digital
twins do more than merely predict the
issue that may occur; they also propose
the corresponding solution. Digital
twins will play a significant role in the
development of future smart factories
capable of making autonomous decisions
about what to make, when and how, in
order to maximize customer satisfaction –
and profitability.
Early adopters of digital twins commonly
report benefits in three areas:
■ Data-driven decision making and
collaboration
■ Streamlined business processes
■ New business models
Figure 5: GE’s digital wind farm project leverages
digital twin technology to make predictions on power
output. Source: Harvard Business Review
Figure 5
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“DRIVING IMPROVED BUSINESS
OUTCOMES WITH DIGITAL TWINS”
by Sam George, Director, Azure Internet of Things, Microsoft
For years now, Microsoft’s partners and customers have been using the cloud and
digital twins to create breakthrough applications for a wide variety of industries.
We’ve learned that most digital
transformation efforts benefit from
context about the physical world by
creating a digital twin. For example,
customers have been creating digital
replicas of buildings, connecting
building systems like heating and
cooling, as well as electrical systems to
space utilization and people within the
building. Energy sector customers are
modeling their distribution grid to even
out usage spikes and ensure
substations aren’t over utilized,
enabling them to avoid unnecessary,
costly infrastructure updates. Digital
twins support the entire lifecycle, from
design time, through construction and
commissioning, all the way through to
operations. With these digital twins
customers can then predict the future
state of their models, as well as
simulate potential changes. A digital
twin helps bring value to the various
IoT data into the model which fits their
domain, and without it the IoT data has
less value.
It is critical to connect to the devices
and sensors in the physical world to
provide real-time and operationalized
data – not just the idealized state of the
system. This enables customers to take
advantage of replicas alive with data.
By adding artificial intelligence (AI) to
the mix, customers can identify trends,
forecast the future, optimize and
simulate changes. This enables
customers to save money and improve
planning, products, and customer
relationships.
Because each digital twin presents a single
visualization to key decision makers, it
provides a single source of truth for an
asset that drives stakeholder collaboration
to resolve problems expediently. Digital
twins can be used to automate tedious
error-prone activities such as inspections,
testing, analysis, and reporting. This
frees teams to focus on higher-value
activities. Digital twins are a major driver
of product-as-a-service business models
or servitization – this is when companies
abandon the one-time sale of a product
to instead sell outcomes by managing the
full operation of the asset throughout its
lifecycle. Digital twins allow manufacturers
to monitor, diagnose, and optimize their
assets remotely, helping to improve
availability and reduce service costs.
1.5 THE DIGITAL
TWIN THROUGH THE
PRODUCT LIFECYCLE
Since their inception, digital twins have
been closely associated with product
lifecycle management (PLM). Digital
twins are now used throughout the
full product lifecycle, with a product’s
twin emerging during the development
process and evolving to support different
business needs as a product progresses
through design, manufacturing, launch,
distribution, operation, servicing, and
decommissioning.
Product Development. Data from the
digital twins of previous products can
be used to refine the requirements and
specifications of future ones. Virtual
prototyping using 3D modeling and
simulation allows faster design iterations
and reduces the need for physical tests
as depicted in figure 6. During the
design phase, tests with digital twins can
detect clashes between components,
assess ergonomics, and simulate
product behavior in a wide variety of
environments. Together these measures
help to reduce development costs,
accelerate time to market, and improve
the reliability of the final product.
Production. Digital twins facilitate
collaboration between cross-functional
teams in the manufacturing process.
They can be used to clarify specifications
with suppliers and allow designs
to be optimized for manufacturing
and shipping. If the organization
manufactures a new digital twin with
every product it makes, each model
will incorporate data on the specific
components and materials used in the
product, configuration options selected
by end customers, and process conditions
experienced during production.
Digital twins of production lines as
illustrated in figure 7 allow layouts,
processes, and material flows to be
tested and optimized before a new
manufacturing facility is commissioned. Figure 6: Digital twins enable faster design iterations and rapid prototyping before going into production. Source: Forbes
Figure 6
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Operate & Service. Once the product
passes into the hands of the end-user,
its digital twin continues to accumulate
data on its performance and operating
conditions. This data helps to support
maintenance planning, troubleshooting,
and optimizing product performance.
As products are updated and adapted
or parts replaced, the digital twin
is amended accordingly. Aggregate
information from multiple digital twins
can be analyzed to identify usage trends
and optimize future designs.
End-of-life. When a product is no longer
required by the user, digital twin data
guides appropriate end-of-life actions.
Data on the operating conditions of
specific components informs decisions on
whether to re-use, recondition, recycle, or
scrap these items. Material data can help
to determine appropriate recycling and
waste streams. And the data accumulated
by the digital twin during this process can
be retained for future analysis.
1.6 CHALLENGES IN
APPLYING
DIGITAL TWINS
There are significant challenges to the
widespread adoption of digital twins.
Matching complex assets and their
behavior digitally – with precision
and in real time – can quickly exceed
financial and computing resources,
data governance capabilities, and even
organizational culture. This section
identifies the stumbling blocks that may
be encountered when leveraging digital
twins.
Cost. Digital twins require considerable
investment in technology platforms,
model development, and high-touch
maintenance. While most of these costs
continue to fall, the decision to implement
a digital twin must always be compared
to alternative approaches that might
deliver similar value at lower cost. If a
company is interested in a small number
of critical parameters, these insights may
be gathered more cost effectively via
an IoT system based on sensors and a
conventional database.
Precise Representation. For the
foreseeable future, no digital twin will be
a perfect representation of its physical
counterpart. Matching the physical,
chemical, electrical, and thermal state of a
complex asset is an extremely challenging
and costly endeavor. This tends to force
engineers to make assumptions and
simplifications in their models that
balance the desired attributes of the twin
with technical and economic constraints.
Data Quality. Good models depend on
good data. That may be a difficult thing
to guarantee in digital twin applications
which depend on data supplied by
hundreds or thousands of remote
sensors, operating in demanding field
conditions and communicating over
unreliable networks. As a minimum,
companies will need to develop methods
to identify and isolate bad data, and to
manage gaps and inconsistencies in
product data streams.
Figure 7
Figure 7: Siemens is applying digital twin technology to
optimize processes within its production lines. Source:
Siemens
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Interoperability. Despite significant
progress in openness and standardization,
technical and commercial barriers to
the exchange of data remain. And where
a digital twin relies on simulation or
AI technologies supplied by a specific
vendor, it may be difficult or impossible
to replicate that functionality using
alternative providers, effectively locking
companies into long-term single-supplier
relationships.
Education. The use of digital twins
will require staff, customers, and
suppliers to adopt new ways of working.
That presents challenges in terms of
change management and capability
building. Companies must ensure users
have the skills and tools they need to
interact with digital twins and must
be sufficiently motivated to make the
necessary transition. Leveraging the new
technologies required for digital twins
typically requires a profound cultural
shift to fully realize the value afforded
by this change. For more on this topic,
see the expert viewpoint on this page by
Janina Kugel, Board Member and Chief
HR Officer of Siemens on leadership and
digital skills in times of technological
transition.
IP Protection. A digital twin is a reservoir
of intellectual property and know-how.
The models and data incorporated into a
twin include details of a product’s design
and performance. It may also contain
sensitive data on customer processes and
usage. That creates challenges around
data ownership, identity protection, data
control, and governance of data access by
different user groups.
Cyber Security. Digital twins will be
tempting targets for cyber criminals. The
data links that connect physical objects
to their twins provide a new point of
entry for malevolent actors seeking to
disrupt an organization’s operations.
Where digital twins play a role in the
control of their physical counterparts,
compromising a twin may have direct
and potentially devastating real-world
impact. Those characteristics make
effective management of digital twin
cyber security a critical priority, and one
that will present new challenges to many
organizations.
Every technology wave is accompanied
by its own unique set of challenges and
digital twins are no exception to this
pattern. Industries with core offerings
that depend on complex products and
machinery were first to leverage the
benefits of digital twins. Starting from
humble beginnings in the aerospace and
defense industries, digital twins are now
enhancing operational value chains in
the engineering, manufacturing, energy,
and automotive industries. While today
digital twins might seem like a far cry for
the logistics industry, the next chapter
examines current best practices in
several different industries that logistics
professionals can potentially learn from.
“CHANGING TASKS AND LEADERSHIP
IN TECHNOLOGICAL TRANSITIONS”
by Janina Kugel, Board Member and Chief HR Officer, Siemens
Digital twins are likely to create a technology transition in any organization that
implements them. It is of paramount importance that we look at the opportunities
technology shifts like digital twins bring, rather than the pitfalls. We’ve got to be
open for new things and leverage the opportunities afforded by technology in such a
way that they help us increase the quality of life and benefit society and companies.
Today, the biggest challenge of the
technological transition is the rapid pace
of change. Much of what we experience
now was considered science fiction 20
years ago, digital twins included. We must
do some things differently than we have
done in the past, and this can’t happen
without a culture change.
It’s a journey that consists of many little
steps. We need to break down silos by
maintaining a global network and
exchanging ideas, within our own
company and beyond it. We need to
flatten hierarchies and enable flexible
working. Managers have to guide and
support their teams, encourage them to
try new things and allow errors.
Therefore, managers can’t just
communicate with a small group, they
need to collaborate openly across all
parts of organizations. Collaboration is
critical to leveraging the insights created
by new technology like digital twins.
Finally, we all have to acquire new skills
on a constant basis. This form of
continuing learning is a top priority at
Siemens. And it’s not just about upskilling
but also about reskilling because – unless
we retire within the next twelve months
– we need to be open to learn totally new
things.
In this context, the role of managers
changes. Being a ‘boss’ is not enough,
they have to play multiple roles. They may
be colleagues, mentors, or coaches – or
mentees who learn from others. They
may have to switch back and forth
between these roles several times a day
depending on the situation and on the
specific colleagues they’re dealing with.
In the digital age, soft skills are becoming
more important than ever!
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Chapter 2
Digital Twins Across Industries
Digital twins can ultimately represent any physical thing,
from nanomaterials all the way to entire cities. Even human
beings and their behaviors have been modeled by digital
twins in some cases. Organizations in multiple sectors
are developing, testing, and utilizing digital twins within
their operations. The following examples show how digital
twins have the potential to solve a broad range of business
challenges and to unlock many different sources of value.
Page 12/39
2.1 DIGITAL TWINS IN
MANUFACTURING
Manufacturing operations have been a
particular area of focus for digital twin
development. In part, that’s because
factories are data-rich environments in
which core business is the production
of physical assets. Companies make
extensive use of automation and robotics
systems on production lines and many
plants are adopting IoT technologies
and using digital information to
optimize production performance. In
high-productivity manufacturing there
is plenty of value at stake. Even small
improvements to throughput, quality,
or equipment reliability can be worth
millions of dollars.
CNH Industrial, a global producer of
agricultural, industrial, and commercial
vehicles, has used digital twins to
optimize maintenance at its plant in
Suzzara, Italy, where it produces Iveco
vans. The company worked with a
consultant, Fair Dynamics, and a software
provider, AnyLogic, on a pilot project to
improve the reliability of robot welding
machines on the plant’s chassis line, as
depicted in figure 8.
While the project was partly intended
as a technology demonstrator, CNH
also hoped to solve a serious reliability
challenge. Its welding robots rely on
a flexible copper conductor called a
lamellar pack to deliver electrical current
to their welding heads. But these packs
have a finite life and accumulated wear
can cause a pack to melt, disrupting
production and damaging the robot.
To determine the most efficient way to
maintain these critical components, the
company built a digital twin model of the
line. Its model includes the different types
of chassis and their associated welding
requirements, the automatic welding
stations distributed along the line, and
the individual robots in each station. Data
for the model is supplied by the plant’s
production planning systems and by
condition monitoring sensors fitted to
each robot. Using simulation and machine
learning, the digital twin forecasts the
probability of component failure. This
system allows the company to run what-if
scenarios comparing different operation
and maintenance regimes in order to
optimize maintenance and spare parts
expenditure while minimizing both
planned and unplanned downtime.
Baker Hughes, a GE company that
produces equipment for the oil and gas
industry, has used technologies from its
parent company to build a comprehensive
digital twin of its plant in Minden, Nevada,
USA. The model incorporates data from
thousands of machines and processes
across the facility, as well as data on
component deliveries from suppliers.
By providing a comprehensive real-time
view of factory performance, the digital
twin helps managers and staff to find
improvement opportunities and react
quickly to issues as they arise. Baker
Hughes says that the approach has helped
to improve on-time delivery at what was
already a high-performing facility. Over
time, the company’s ambition is to double
the rate at which materials flow through
the plant, with the help of continued
optimization through the digital twin.
Figure 8
Figure 8: A digital twin of the Iveco manufacturing
line helps drive better automated welding capabilities.
Source: AnyLogic
Page 13/39
“HOW DIGITAL TWINS WILL POWER
THE FACTORIES OF THE FUTURE”
by Eric Seidel, Vice President and General Manager,
Lifecycle Solutions & Technologies, Honeywell
Factories generate gigabytes of data every day, from their processes and
production assets and even from the workflows of their people. Today, much of
that data is locked away in disconnected systems. The factory of the future will
connect all its data seamlessly, and it will extend those connections across the
whole supply chain, upstream to suppliers and downstream through logistics to
customers. Digital twins of factories and supply chains create three major
opportunities for manufacturing organizations.
First, digitalizing the physical
environment and visualizing its digital
twin enables actionable insights and
much faster decision-making. That will
eliminate a huge amount of the
variability that companies have to deal
with today. In the factory of the future,
every day will be your best day of
performance.
Second, digital twins will empower the
factory workforce, by providing
dramatically improved access to data
and information. This will be
increasingly delivered with advanced
training and operator support
technologies such as virtual reality (VR)
systems and augmented reality (AR)
technologies that overlay information
on the worker’s field of view. In the
factory of the future, every employee
can act as a leading expert.
Once you are operating at the level of
your best day every day and every
employee is as effective as your leading
expert, you can pursue the third, and
biggest, opportunity. Companies can
start to compare their own data with
data from other factories around the
world. You can ask ‘how well does the
best facility in the world perform?’.
Then you can apply continuous
improvement to enhance the output or
the throughput of the plant until your
performance matches the very best
that can be achieved.
2.2 DIGITAL TWINS IN
MATERIALS SCIENCE
The performance of physical products
depends on the characteristics of their
materials. Advances in materials science
underpin many of the technologies
we rely on today. Strong, lightweight
materials are helping to reduce fuel
consumption in cars, trains, and
aircraft. And long-lasting bearings
benefit from accurately formulated and
processed steel alloys. But the precise
characteristics of a specific piece of
material is difficult to determine. Testing
can be destructive or require specially
prepared samples, making it hard to
undertake with real parts or in the
production environment.
Digital twins may provide a solution to
this challenge. German software company
Math2Market has developed specialized
software for the simulation of a variety
of material properties. The company’s
GeoDict software models structurally
complex materials including nonwoven
fabrics, foams, ceramics, and composites.
Using AI-assisted image processing
techniques, GeoDict depicted in figure 10
captures details of the interior geometry
of these materials from computeraided tomography (CAT) scans, electron
microscopy, and similar image sources.
It uses these material digital twins for a
wide variety of purposes, from strength
and stiffness analysis to fluid dynamics
studies that model the flow of liquids and
gases through filters. Customers in the
oil and gas industry are also using the
approach to model flows through porous
rocks in underground reservoirs.
Meanwhile in the aviation industry, new
materials are needed in the quest for
increasingly lightweight, fuel-efficient,
yet sturdier aircraft. Figure 9 shows how
the carbon-fiber composite fuselage
of a Boeing 787 passenger aircraft is
effectively woven together at a production
facility. Extensive digital simulation and
optimization of the material composition
was needed achieve the weight and
robustness required by the commercial
aviation industry.
Figure 9
Figure 9: Simulating composite material performance
is critical to ensure airworthiness. Source: Spirit
Aerosystems / YouTube
Page 14/39
2.3 DIGITAL TWINS IN
INDUSTRIAL PRODUCTS
Besides manufacturing, another killer
app for digital twin technologies is
industrial products. This is largely because
the companies with these assets often
pursue servitization strategies and these
necessitate product uptime.
Digital twins support servitization by
allowing these companies to monitor
their products when they are in the
customer’s hands. Digital twins enable
efficient maintenance strategies and
remote diagnosis and repair. In some
applications, customers may even be
willing to pay for the data or insights
generated by the digital twins of the
manufacturer-owned assets.
Major aero engine manufacturers
including Rolls-Royce, GE, and Pratt &
Whitney are among the most advanced
users of digital twin technologies today.
They are applying digital twins in new
product development, in manufacturing
and, of course, to assist the monitoring
and support of engines operating on
customer aircraft.
Digital twins are also coming into use
on the ground. Compressed air systems
manufacturer Kaeser Compressoren
in the Netherlands has worked with
SAP to develop a digital twin solution
encompassing its entire sales and product
support lifecycle, for example. This
system acts as a repository for documents
and data created during the specification
and tendering process for a new
installation, and the platform provides
remote monitoring and predictive
maintenance capabilities.
Figure 10
Figure 11
Figure 10: Material digital twins allow in-depth simulation of material properties. Source: Math2Market
Figure 11: Digital twins accelerate the quality of MRO services. Source: MRO Network
Page 15/39
Data
Predictive Maintenance
& Optimized Operations
Device Diagnostics
& Prognostics
Design & Performance
Improvements
Sensors
Digital twin of a CT scanner
Physical
device
Digital
twin
2.4 DIGITAL TWINS IN
LIFE SCIENCES AND
HEALTHCARE
Researchers and clinicians across the
healthcare sector are exploring the
potential of digital twins, too. Much of this
research focuses on modeling aspects
of the human body. Such models can
help doctors understand the structure
or behavior of the body in greater detail,
while reducing the need to perform
invasive tests. Digital twins can allow
complex operations to be rehearsed
safely. They also have the potential to
speed drug development by allowing new
therapies to be evaluated ‘in silico’.
Siemens Healthineers has developed
digital twin models of the human heart
like the one in figure 13. The system
simulates the mechanical and electrical
behavior of the heart, and uses machinelearning techniques to create unique,
patient-specific models based on medical
imaging and electrocardiogram data.
The Siemens team is reaching the end
of a six-year study in which it made
digital twins of 100 patients undergoing
treatment for heart failure. At this stage,
the team is simply comparing predictions
made by the models to the outcomes
seen in the patients, but future trials could
eventually result in a role for digital twins
in diagnosis and treatment planning.
Medical device maker Philips has its own
project underway to create a digital twin
of the heart and the company is exploring
a range of additional digital twin
applications. In product development,
for example, it is using modeling and
simulation to test virtual prototypes.
And it is applying artificial intelligence
techniques to facilitate remote support of
complex equipment such as CT scanners
that perform magnetic resonance
imaging (MRI) like the one depicted in
figure 12. In use, a scanner may generate
800,000 log messages every day. Philips
uploads that data from customers and
analyses it to look for early warning signs
of problems in its machines.
Figure 12
Figure 13
Figure 12: Digital twin of a CT scanner. Source: Philips
Figure 13: Using digital twins to replicate human physiology allows us to understand and test the best potential therapy
conditions. Source: Siemens Healthineers
Page 16/39
Other players are using digital twin
approaches developed in manufacturing
applications to improve the productivity
and effectiveness of hospitals and
similar healthcare settings. Mater Private
Hospitals in Dublin, Ireland, for example,
created a digital twin of the radiology
department with the help of another
team from Siemens. The group used
operational data from the hospital to
model the workflows of staff and patients
through the department, then conducted
a series of what-if analyses to examine
the impact of layout modifications and
changes to the nature and volume of
demand. Elsewhere, GE Healthcare says
the digital twin technology built into its
Hospital of the Future Analytics Platform
allows modeling and simulation of entire
hospital workflows for the first time.
Some life sciences groups are even more
ambitious. The DigiTwins initiative, a
large-scale research consortium involving
118 companies and academic institutions
worldwide, has set itself the goal of
producing a personal digital twin for
every European citizen. The project aims
to develop new modeling technologies,
medical informatics systems, and data
sources to dramatically improve doctors’
ability to diagnose illnesses and select
appropriate treatments. If the project
succeeds, the prize could be huge. Its
founders point out that the inability to
correctly predict the effects and sideeffects of drugs on individual patients
costs Europe’s healthcare systems around
€280 billion a year, 20 percent of its total
budget.
2.5 DIGITAL TWINS IN
INFRASTRUCTURE AND
URBAN PLANNING
Measured by the size of the physical
objects they represent, some of today’s
largest digital twins are replicas of
physical infrastructure, such as energy
and transport networks and urban
environments.
In the UK, rail equipment company
Alstom has built a digital twin to simplify
the management of its train maintenance
operations on the West Coast Main Line
depicted in figure 14. One of Britain’s
busiest inter-city rail routes, the line
connects London to Glasgow and
Edinburgh via major cities in the English
Midlands and North West. Ensuring
maximum availability of the fleet of
56 Pendolino trains is a continuous
challenge.
Alstom’s digital twin includes details of
every train in the fleet, along with their
operating timetables and maintenance
regimes. It also models the available
capacity at each of Alstom’s five
maintenance depots. Running inside
the AnyLogic simulation environment,
the model uses a heuristic algorithm
to schedule maintenance activities and
allocate them to the most appropriate
depot. Because the system is connected
to live information on train locations
and planned movements, it can
continually adapt maintenance plans to
accommodate urgent repairs. Operational
issues may mean a particular train is
elsewhere on the network when work is
required. Maintenance planners also use
the system for what-if analyses to explore
the impact of changes to maintenance
strategies and train timetables.
Figure 14
Figure 14: Alstom leverages digital twins to optimize
maintenance regimes and capacity in the UK. Source:
Place North West
Page 17/39
Finnish electricity transmission system
operator Fingrid worked with IBM,
Siemens, and other partners to build a
digital twin of Finland’s power network.
The Electricity Verkko Information
System, or ELVIS, combines eight
different systems as depicted in figure
15 into a single application, providing
Fingrid with a consistent, comprehensive,
and continuously updated model of
its network. The digital twin is used in
day-to-day grid operations, helping staff
to manage power flows and protection
settings to meet demand without
overloading transformers or transmission
lines. It also supports design and planning
activities, allowing the operator to
simulate the likely impact of changes
to grid configuration or investment in
upgraded assets.
In India, the state of Andhra Pradesh is
developing a digital twin of a brand new
city. Designed by architects Foster and
Partners, Amaravati will serve as the new
state capital, required after changes to
regional borders carved off the original
capital, Hyderabad, into the new state of
Telangana. Amaravati has ambitions to
become one the most digitally advanced
cities in the world, with development
planning and operations all running on a
single platform.
The initial prototype of the digital twin
shown in figure 16, which uses Smart
World Pro software from Cityzenith, will be
completed in 2019. During construction,
the platform will collect data from IoT
sensors to monitor environmental
conditions and the progress of work.
Ultimately, the project’s backers say
the system will run the city’s traffic
management systems and automate tasks
such as ensuring planning applications
comply with local rules.
Data sources
enabling electrical grid
digital twins
Operations
Planning
Operations
Planning
Protection
Asset
Management
GIS
Motor Data
Management
Market
External Entities
e.g. Neighboring
TSO / ISO / RTO
Outages
Relay
Settings Substation
As-Built
Line Ratings
Devices
Other Data
Sources
Data Sources
Adapters / Interfaces
Electrical Digital Twin
Engine & GUI
Electrical Digital
Twin Database
Figure 15 Figure 16
Figure 15: Data sources enabling electrical grid digital twins. Source: Siemens
Figure 16: A digital twin provides a rich single-pane-of-glass view of an urban project. Source: SmartCitiesWorld
Page 18/39
2.6 DIGITAL TWINS IN
THE ENERGY SECTOR
Energy production, whether by fossil
fuels or renewables, involves big, complex
assets, often in remote locations. Those
characteristics are driving the exploration
and adoption of digital twins as a way
to improve reliability and safety while
keeping operating costs under control.
In the offshore oil and gas sector, for
example, Aker BP used Siemens analytics
technology in its Ivar Aasen project off
the Norwegian coast, shown in figure 17.
The success of the project, which reduces
manpower requirements on the platform
and optimizes equipment maintenance
schedules, led to a strategic agreement
between the two companies. Under the
agreement, Aker BP and Siemens will
develop digital lifecycle automation and
performance analytics solutions for all
future assets in the field.
Elsewhere in the North Sea, Royal Dutch
Shell is involved in a two-year project
to develop a digital twin of an existing
offshore production platform shown in
figure 18. In the Joint Industry Project, the
organization is partnering with simulation
company Akselos and engineering
R&D consultancy LIC Engineering to
implement new approaches to managing
the structural integrity of offshore
assets. The pilot project involves the
development of a structural model of
the platform, which will use data from
sensors to monitor its health and predict
its future condition.
Figure 17 Figure 18
Figure 17: A visualization of the digital twin technology
leveraged in the Ivar Aasen project. Source: Siemens
Figure 18: Shell will develop digital twins of existing oil
platforms to manage these platforms more effectively.
Source: World Oil
Page 19/39
In the wind energy sector, meanwhile,
digital twin solutions are helping
companies manage bigger turbines and
meet aggressive reliability and costreduction targets. Norwegian engineering
consultancy DNV GL Group, for example,
has developed WindGEMINI, a digital
twin package that includes physics-based
models to monitor the structural integrity
and predict the remaining fatigue life
of turbines and components. GE is also
exploring the potential of digital twins
in its own wind turbine business. In one
pilot project, the company built a thermal
model of key wind turbine components,
allowing engineers to create virtual
sensors that estimate the temperature of
inaccessible components based on data
from physical sensors installed nearby.
2.7 DIGITAL TWINS IN
CONSUMER, RETAIL
AND E-COMMERCE
Consumer products present a different
set of opportunities and challenges for
digital twin technologies. Instead of a
few large and complex assets, consumer
markets often involve millions of much
simpler objects. So players in this
sector have a different focus – they are
exploring the development of digital
twins that track the flow of products
through supply chains, for example, or
building systems that can extract valuable
insights from aggregated data produced
by large numbers of comparatively simple
models.
Dassault Systèmes, a producer of 3D
modeling and digital twin technologies,
has worked with retail technology
company Store Electronic Systems to
develop digital twins of retail stores. In
the system, a 3D model of a store’s layout
is populated with data from electronic
shelf labels like those in figure 19 to
produce a detailed virtual representation
of the store and its contents. Links to
the store’s inventory and point-of-sale
systems update the model in real time to
reflect the number of items stocked on
the shelves. Dassault says its approach
has multiple potential uses including
applications that guide shoppers to the
items on their list. The solution can also
reduce stock-outs and help to improve
merchandise layouts.
In retail and e-commerce applications,
meanwhile, companies are developing
increasingly sophisticated models
of consumer behavior. Data on past
purchases, web browsing habits, and
social media activity is already being used
by many companies to target advertising
and promotions for specific customers
and contexts. Over time, these models
may evolve into consumer digital twins:
complex, multi-attribute behavioral
models that will attempt to predict future
behaviors and proactively influence
purchase decisions.
Figure 19: Electronic store labels act as critical sensors to feed the digital twins of retail spaces. Source: SES
Figure 19
Page 20/39
Chapter 3
Digital Twins in Logistics
While digital twins have not yet achieved widespread
application in logistics, many of the key enabling
technologies are already in place. The logistics sector has
leveraged sensors to track shipments and more recently
machinery and material handling equipment. Today the
industry is also increasingly embracing open API strategies
and migrating to cloud-based IT systems. Companies
are applying machine learning and advanced analytics
techniques to optimize their supply chains and draw new
insights from historical shipment and operational data.
Logistics professionals are even implementing augmented,
mixed, and virtual reality applications for tasks like
warehouse picking and vehicle loading as the data from
these tasks is well suited to the creation of digital twins in
these environments.
Page 21/39
Bringing these and other technologies
together into a full digital twin
implementation is a complex and
challenging endeavor, however. The
cost-sensitive nature of many logistics
activities may explain why few companies
have so far been willing to make the
necessary investments.
It is clear that the exploration of potential
use cases for digital twins across the
logistics space is now worthwhile. As
costs fall and confidence in technology
grows, the business case for some or
all of the approaches described in this
chapter may become compelling within
the coming years.
3.1 PACKAGING &
CONTAINER DIGITAL
TWINS
The overwhelming majority of products
that move through logistics networks do
so in some form of protective enclosure.
The industry employs large quantities of
single-use packaging together with fleets
of dedicated or general-purpose reusable
containers.
Designing, monitoring, and managing
packaging and containers creates a
number of challenges for the industry.
The growth of e-commerce, for example,
is driving up demand, seasonal volatility,
and packaging variety. This, in turn,
produces significant waste and reduces
operational efficiency through poor
volume utilization.
The application of material digital twins
could aid the development of stronger,
lighter, more environmentally friendly
packaging materials. In efforts to improve
sustainability, companies are exploring
the application of a range of new
materials including compostable plastics
and materials with a high percentage of
post-consumer recycled content.
Furniture giant IKEA is even replacing
plastic foam with a biological alternative
grown from mushrooms.
Material digital twins such as those
developed by Math2Market could help
companies understand and predict
the performance of new materials in
packaging applications. These twins
can model material behavior under the
temperature, vibration, and shock loads
experienced in transit.
Digital twins could also help logistics
players manage container fleets more
efficiently. Reusable containers are an
industry standard in multiple logistics
flows and modes. They include standard
ocean containers, aircraft ULDs, reusable
crates to transport car parts between
factories, and containers for food and
beverage delivery to retail stores and
consumer homes.
Keeping track of reusable containers can
be difficult. Not only must companies
handle the movement of containers from
their last destination to where they are
needed next, but they must also check for
damage and contamination that might
compromise future loads or present a
hazard to personnel or other assets.
Emerging 3D photographic technologies,
such as those developed by German
startup Metrilus, can rapidly create a
detailed model of a container, allowing
the automated identification of potential
problems such as dents and cracks.
That information could be combined
with historical data on the container’s
movements to create a digital twin that
informs decisions about when a specific
asset should be used, repaired, or retired.
Moreover, aggregating such data across
a whole fleet of containers could help
owners to make optimal decisions about
fleet sizing and distribution, and identify
trends that might indicate underlying
problems such as a flaw in container
design or rough handling that occurs at
specific points in the supply chain.
3.2 DIGITAL TWINS OF
SHIPMENTS
Incorporating the contents of a package
or container into its digital twin is the next
logical step. If a digital twin of an item
to be shipped has already been created,
data describing its geometry can be
obtained from this pre-existing source,
for example. Alternatively, the item data
can be generated when the shipment
is prepared, using 3D scanning and the
same computer vision technologies
mentioned in the previous section.
Combining product and packaging data
could help companies improve efficiency,
for example by automating packaging
selection and container packing strategies
to optimize utilization and product
protection.
Figure 20: Digital twins of sensitive shipments will
bring next-level visibility to the item and its packaging
during transit. Source: Finnair Cargo
Figure 20
Page 22/39
It is already common practice to ship
sensitive, high-value products, such as
pharmaceuticals and delicate electronic
components, with sensors that monitor
temperature, package orientation, shock,
and vibration. The latest variants of
these sensors, such as those developed
by Roambee, Blulog, Kizy and others
incorporate sensors that offer a growing
host of data points that allow continuous
data transmission during the progress of
a shipment.
A shipment digital twin would act as
a repository for the data collected by
these sensors. Digital twin technologies
could also allow this data to be used in
new ways. A model that includes the
thermal insulation and shock-absorbing
characteristics of the packaging, for
example, could allow conditions inside
the product be extrapolated from data
collected by external sensors.
3.3 DIGITAL TWINS
OF WAREHOUSES
AND DISTRIBUTION
CENTERS
Digital twins could have a significant
impact on the design, operation, and
optimization of logistics infrastructure
such as warehouses, distribution centers,
and cross-dock facilities. These digital
twins could combine a 3D model of the
facility itself with IoT data collected in
connected warehouse platforms (figure
21), as well as inventory and operational
data including the size, quantity, location,
and demand characteristics of every item.
Warehouse digital twins can support
the design and layout of new facilities,
allowing companies to optimize space
utilization and simulate the movement
of products, personnel, and material
handling equipment.
During warehouse operations, the digital
twin can be constantly updated with data
harvested from the various automation
technologies that are becoming more
prevalent in warehouses. These include
drone-based stock counting systems,
automated guided vehicles, goods-toperson picking systems, and automated
storage and retrieval equipment.
Digital twins will also allow further
optimization of the performance of
these automation systems, for example
by using sensor data, simulation, and
monitoring technologies to reduce energy
consumption while maintaining requisite
throughput levels.
Comprehensive 3D facility data can also
be used to enhance the productivity of
warehouse personnel. Companies can
deploy virtual-reality training tools, for
example, or augmented-reality picking
systems using wearable devices such
as Google Glass Enterprise Edition or
Microsoft HoloLens – tools that are
already being utilized today by DHL
Supply Chain.
Figure 21 Figure 22
Figure 21: DHL uses heat maps based on internet of
things technology to optimize operational efficiency
and lay the foundations for safer working practices in
warehouses. Source: DHL
Figure 22: Augmented and virtual reality glasses used
for vision picking provide a valuable data stream to
leverage in warehouse digital twins. Source: DHL
Page 23/39
Digital twins in logistics In logistics, the ultimate
digital twin would be a model of an
entire supply chain network
Airport
Container
Vessel
Train
Warehouse
Shipment
Delivery Van
Truck
Port
Figure 23: A visionary example of the
elements involved in a digital twin of an
entire supply chain network. Source: DHL
Page 24/39
Perhaps the most compelling argument
for using digital twins in warehouses and
similar facilities is their contribution to
continuous performance improvement.
Comprehensive data on the movement
of inventory, equipment, and personnel
can aid the identification and elimination
of waste in warehouse operations,
from congestion in busy aisles to
low productivity or picking errors by
personnel. Before making changes on
the ground, simulation using digital
twins can enable facility managers to
test and evaluate the potential impact of
layout changes or the introduction of new
equipment and new processes.
In environments such as e-commerce
fulfillment that must accommodate
rapid changes to volumes and inventory
mix, digital twins can also support
dynamic optimization of operations.
Stock locations, staffing levels, and
the allocation of equipment can be
continually adjusted to match current or
forecasted demand.
3.4 DIGITAL TWINS
OF LOGISTICS
INFRASTRUCTURE
Warehouses and distribution centers
make up just a fraction of all logistics
infrastructure. The flow of goods from
source destination depends on the
orchestration of multiple elements
including ships, trucks and aircraft, order
and information systems, and, above all,
people.
This complex, multi-stakeholder
environment can be seen most clearly at
major global logistics hubs such as cargo
airports and container ports. At these
facilities today, the challenge of efficient
operation is exacerbated by imperfect
systems for information exchange, with
many participants reliant on offline
processes that can be subject to errors
and delays.
A project is now underway in Singapore to
use digital twin technologies to address
these problems. The Singapore Port
Authority is working with a consortium
of partners, including the National
University of Singapore, to develop a
digital twin of the country’s new megahub for container shipping.
The university’s Professor Lee Hoo
Hay shown in figure 25 is leading
the initiative’s technical and research
development. He says that digital twin
technology is finally becoming a reality
thanks to a confluence of technology
advancements. “Simulation-based
optimization, industry 4.0, and the
internet of things have been around for
some time now. However it has really
been the boom of artificial intelligence
and its predictive capabilities that have
given digital twins a big push in creating
new value. In the past, creating spatial
models digitally was exciting, but failed to
be more than a way to visualize an object
statically. Today, all the data we have
from sensors, historical performance,
and inputs about behavior lends itself to
being linked to the spatial model and to
predicting future behavior by changing
different inputs. Effectively the data and
prediction capabilities make the spatial
model come alive.”
Figure 24
Figure 24: The already advanced Singapore port is
about to get a major upgrade with digital twins. Source:
CraneMarket
Page 25/39
The digital twin approach is already
delivering benefits during the design
phase of the Singapore project. The
consortium is using its digital models
to expedite the generation of potential
layouts, and it is using simulation systems
to evaluate different operating scenarios.
Eventually, the Port Authority hopes
that the digital twin will help it optimize
management of the new facility. Using
simulation, for example, it will be able to
choose the optimum berthing location
for a vessel of any given size, taking into
account the assets, space, and personnel
required for loading and unloading
operations, and the need to share those
resources between multiple vessels at
any one time.
While Singapore has a bold vision for
the application of digital twins in largescale logistics infrastructure, the ultimate
success of any such initiative depends on
the willingness – and technical ability – of
all the stakeholders involved. A ‘living’
digital twin of a port or airport will require
every organization using the facility to
operate and maintain a digital twin of its
own assets and personnel, and to share
relevant data in real time with other
users.
3.5 DIGITAL TWINS OF
GLOBAL LOGISTICS
NETWORKS
In logistics, the ultimate digital twin
would be a model of an entire network
including not just logistics assets but also
oceans, railway lines, highways, streets,
and customer homes and workplaces.
The idea of such an all-encompassing
twin even like the one depicted earlier in
this chapter is largely an aspiration for
the logistics industry for now. However,
it is important to envisage where the full
realization of logistics digital twins may
lead.
Geographic information system (GIS)
technology is progressing extremely
rapidly today, enabled by advances in
satellite and aerial photography, and by
on-the-ground digital mapping efforts.
Demand for detailed geographical data
has been driven by many user groups
including governments, utility companies,
and navigation system providers. Most
recently, the development of autonomous
vehicle technologies has accelerated
efforts to produce extremely detailed
data of the globe. Self-driving cars
and trucks are already being trialed on
public roads, with 11 major carmakers
announcing plans to launch vehicles with
autonomous capabilities over the next
decade.
Autonomous vehicles will transform the
availability of geographical data in two
ways. They will require extremely detailed
maps to operate, and they will also
perform mapping functions themselves,
collecting data from on-board cameras
and from radio or light-based detection
and ranging systems (radar and lidar
methods) and then sharing that data
wirelessly to continually update and
improve map databases.
Modern GIS systems are much more
than static digital maps. They can
also incorporate dynamic data such
as information on traffic speeds and
densities, road closures, and parking
restrictions due to accidents and repair
works. They can even integrate the
real-time location of specific people and
vehicles. Logistics providers already
make extensive use of GIS data, using it
to plan delivery routes, for example, and
predict arrival times based on weather
conditions, congestion, and known delays
at ports, airports, and border crossings.
Digital twins of networks will also help
providers to optimize their conventional
logistics networks, for example by
using rich data on customer locations,
demand patterns, and travel times to plan
distribution routes and inventory storage
locations.
Clearly these aspirations will not be
easy to fulfill and likely are still years
away from full implementation. Most of
today’s digital twins are far less ambitious
in scope but nevertheless present
their users with challenges in terms of
computing resource, data quality, precise
representation, and governance. Here
it is also important to mention that the
heterogeneous and fragmented nature of
the logistics industry will make it a highly
adverse environment for digital twins to
thrive. It is not yet clear whether those
problems can be adequately addressed to
enable the application of digital twins at
truly global scale.
Figure 25
Figure 25: A digital twin to test the upcoming Tuas
mega port design. Source: The Straights Times
Page 26/39
Chapter 4
Logistics Implications of Implementing
Digital Twins
Digital twin technologies have the potential to transform
almost every industry, with engineering, manufacturing,
energy, and automotive leading the charge. As digital twins
enter widespread use, their impact will be felt at every stage
in the value chain. Detailed, real-time data on product use
patterns and operating conditions will help manufacturers
refine and improve their designs. Manufacturing
processes will be faster and more flexible. Data on product
performance will enable a more proactive approach to
maintenance and support, allowing companies to offer their
customers new types of service, or to intervene earlier to
prevent failures and reduce downtime.
Page 27/39
To realize these benefits, however,
companies must be able to translate
digital insights coming from upstream
into physical actions downstream. That
will require significant changes to supply
chains, and to the logistics systems that
manage the flow of materials, parts, and
products through those chains.
4.1 INBOUND TO
MANUFACTURING
Faster, more flexible manufacturing
operations will place new demands on
inbound material flows. Digital twins will
enable more products to be configured
and customized to match the specific
requirements of individual customers,
for example, but fulfilling that demand
will increase complexity, with a greater
number of component variants and more
parts that must be managed in a batch
size of one.
Companies will need to find ways
to handle that complexity without
compromising lead times, reducing
transport efficiencies, or building high,
costly inventories. That will require
care in the choice of supplier locations,
along with new approaches to transport
and freight management. By pooling
transport across multiple suppliers,
for example, companies may be able
to increase utilization even when they
require frequent deliveries and small
order quantities.
Closer collaboration with suppliers
will also be important. Manufacturers
can facilitate this by sharing demand
forecasts – derived in part from digital
twin data – earlier, and by working
closely with suppliers to understand
the capabilities and limitations of
their production processes. Suppliers,
meanwhile, can use approaches such
as vendor-managed inventory (VMI) to
provide additional flexibility and value to
their customers.
Figure 26: Digital twins will drive greater configuration and customization, and this will require more flexibility and
better quality control for fulfilling orders. Source: DHL
Figure 26
Page 28/39
DHL EXPERT VIEWPOINT
Klaus Dohrmann
Vice President
Sector Development Engineering, Manufacturing & Energy
DHL Customer Solutions & Innovation
Digital twins are already changing the way our customers do business. In sectors such as aerospace, manufacturing,
and industrial products, digital twins are becoming a central element of the engineering and in-field support
activities of many organizations. We are now seeing the impact of these new approaches on the supply chain
strategies and service requirements of those companies.
The real impact is yet to come, however. In the near future,
we expect the use of digital twins to grow exponentially,
from individual applications to eco-systems, connecting
assets in operations and entire supply chains from end to
end. That will unlock a multitude of opportunities across all
industry sectors: increasing productivity, reducing waste,
enabling new business models and, most importantly,
delivering a new level of customer experience.
There will be a significant supply chain and logistics
management component to many of these opportunities.
Organizations will need their supply chains to translate
upstream digital insights into physical benefits for their
customers downstream. Logistics service providers will
have a crucial role to play in this part of the digital twin
revolution. Quite literally, it will be up to logistics
professionals to deliver the full value of digital twins.
Given the complexity of the assets involved and the speed
of response required, the support of digital twin-enabled
assets will require the use of advanced logistics concepts
such as control towers, 4PL providers, and lead logistics
partners. Where digital twins enable predictive
maintenance and support capabilities, for example,
companies will need sophisticated service logistics
solutions to deliver that support on the ground.
In every case, close collaboration between all players in the
value chain will be essential to capture the full potential of
digital twins. This will include the early involvement of
logistics specialists in the development and
implementation of digital twin concepts. Our own sector
stands to benefit, too. Digital twins of supply chain assets
– from containers and warehouses to trucks, ships, and
aircraft – will increase the efficiency, flexibility, and
responsiveness of logistics operations.
4.2 IN-PLANT
LOGISTICS
The demands of digital twin-enabled
manufacturing will also place new
requirements on in-plant material flows.
Companies may have to adapt their
processes for just-in-time delivery to
lineside and their kanban replenishment
strategies to accommodate shorter lead
times and higher product complexity.
They will also need to handle material and
component-related data with more rigor,
to ensure that the digital twins of the
products they build are associated with
the correct component serial numbers or
batch codes, for example.
In some cases, adapting manufacturing
operations to accommodate the
requirements of digital twin-driven
products and business models will
require new approaches to the design
of workstations and plant layouts.
Companies may want to switch from
batch processing to single piece flow,
for example, or adapt material storage
and handling systems to cope with
more complex and variable material
requirements. Digital twin technologies
could help companies to manage this
additional complexity, by integrating with
advanced storage and handling systems,
or through the use of AR technologies to
help staff locate and pick parts rapidly.
Figure 27
Figure 27: Digital twins will help optimize in-plant
material flows. Source: DHL
Page 29/39
Figure 28: Companies will need sophisticated logistics
solutions to deliver support on the ground. Source: DHL
Figure 29: Visibility of assets, materials, and shipments
will further improve with digital twins. Source: DHL
Figure 28 Figure 29
Page 30/39
4.3 AFTERMARKET
LOGISTICS
Digital twins have the potential to
redefine the relationship between
product manufacturers and their
customers. With a digital twin, an OEM or
a third-party service partner can monitor
a product anywhere in the world. They
can use that capability to offer a range of
value-adding services to their customers,
from remote support to predictive
maintenance.
These new types of service will be highly
dependent on the effectiveness of the
provider’s aftermarket supply chain,
however. Early warning that a part is likely
to fail is only useful if a replacement is
available for installation at a convenient
time. The supply and distribution of spare
parts will become an increasingly critical
element of the operating model for many
companies.
To build and operate high-performing
aftermarket logistics and support
capabilities, companies will need
to understand exactly where their
customers are, which products they
are using and how they operate those
products. They will need to continually
review the positioning and distribution
of spare parts inventory to guarantee
lead times that match their promises to
customers.
Companies will also need to link parts
distribution tightly with other elements
of their aftermarket and field service
operations. They may need to match
component delivery windows with the
arrival of service technicians at customer
sites, for example, or make greater use of
their dealers and distributor networks to
provide after-sales services.
Aftermarket supply chains will also
need to manage products at the end of
their operating life, whether that is worn
and broken parts removed in service
operations or complete products that
are no longer needed by their original
users. Digital twins can help companies
maximize the potential value of endof-life equipment by helping them to
identify the exact type and content of
equipment. Capturing that value may call
for more sophisticated reverse logistics
processes, integrated with appropriate
remanufacturing, recycling, and waste
management systems.
4.4 ORCHESTRATING
THE SUPPLY CHAIN
By offering a more complete view of
the performance of products across
their lifecycle, digital twins will allow
companies to take a more holistic, endto-end approach to the management of
those products. Maximizing the throughlife value of products and associated
services will require a similarly holistic
approach to supply chains.
In particular, companies will need to
find smarter ways to balance inventory
costs, availability, and lead times across
their networks. That will increase the
importance of full supply chain visibility
so they understand the location and
availability of parts and materials in their
own inventories and those of suppliers,
sales channels, and distribution partners.
Optimum supply chain set up will also be
critical, with supplier and manufacturing
footprints, logistics lanes, and stock
locations configured to support high
service levels and ensure companies can
meet the availability and response time
promises they make to their customers.
Finally, supply chains will need to be
resilient, with the ability to maintain
service levels in the face of disruption,
recover quickly from major events and
respond effectively to changes in demand.
Figure 30
Figure 30: Digital twin-enabled industrial equipment
will accelerate service logistics needs. Source: DHL
Page 31/39
Figure 31
Figure 31: Complete visibility of the performance
of industrial products will drive new approaches to
managing these complex assets. Source: DHL
Page 32/39
Chapter 5
Conclusion & Outlook
Digital twins are today coming of age. Fueled by the
confluence of progress in the internet of things, big data,
cloud computing, open APIs, artificial intelligence, and virtual
reality, once-static digital models and simulations can now
truly come alive in real time to help predict future situations,
the state of physical things, and even the world around us.
Page 33/39
Though challenges and limitations
remain in computing resources, precise
representation, total cost, data quality,
governance, and organizational culture,
industries are evolving to overcome these
obstacles. The benefits with digital twins
will be improved outcomes and powerful
new business models.
Today, the engineering, manufacturing,
energy, and automotive industries are
leading the way in leveraging digital twins
to manage their most critical assets,
followed by healthcare, the public sector,
and even consumer retail. As the requisite
technologies continue to become more
readily accessible, the logistics sector
is only just now beginning its digital
twin journey and early examples of the
first supply chain facilities and logisitics
hubs developed using digital twins are
beginning to emerge.
Perhaps more important for logistics
professionals to consider in the near
term is not how to leverage digital twins
for direct orchestration of supply chain
operations, assets, and facilities but
rather how to evolve the supply chain.
In what ways must supply chains evolve
in organizations when digital twins
become a core part of product, asset, and
infrastructure operation?
As digital twins provide greater insight
into and visibility of the current and
future state of a ‘thing’ – from a material
surface to a critical infrastructure – more
proactive decisions can be made about
managing the thing when deployed in the
field. For digital twins and their physical
counterparts to work together optimally,
there is an accelerating need for logistics
professionals to improve responsiveness,
service quality, availability, and delivery
accuracy to ensure the thing performs in
optimal harmony with its intended design
and performance.
Taking what has been provided in this
report, how will you leverage digital
twins in your organization? How do you
see your supply chain evolving with
the arrival of digital twins? At DHL we
believe logistics digital twins are in their
infancy and, as such, now is the time to
explore and discover what challenges
and opportunities might lay ahead when
embracing digital twins. We look forward
to hearing from you and welcome
collaboration with your organization on
the topic of digital twins.
Page 34/39
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Figure 1: DHL (2019)
The evolution of digital twins.
Figure 2: GE (2017)
GE has created a digital twin of the Boeing
777 engine specifically for engine blade
maintenance. https://www.youtube.com/
watch?v=2dCz3oL2rTw
Figure 3: DHL (2019)
Characteristics of a digital twin.
Figure 4: DHL (2019)
Technologies behind digital twins.
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GE’s digital wind farm project leverages digital
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assets/2015/07/GE-image-6.2.jpg
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Digital twins enable faster design iterations and
rapid prototyping before going into production.
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Figure 7: Siemens (2019)
Siemens is applying digital twin technology to
optimize processes within its production lines.
https://www.plm.automation.siemens.com/
media/global/en/Industrial-Machinery-DigitalTwin-Image_1600x900_tcm27-55683.jpg
Figure 8: AnyLogic (2017)
A digital twin of the Iveco manufacturing line
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Simulating composite material performance is
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Figure 10: Math2Market (2018)
Material digital twins allow in-depth simulation of
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Figure 11: MRO Network (2017)
Digital twins accelerate the quality of MRO
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jpg?itok=0u8LXeGO
Figure 12: Philips (2018)
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com/a-w/about/news/archive/blogs/innovationmatters/20180830-the-rise-of-the-digital-twinhow-healthcare-can-benefit.html
Figure 13: Siemens Healthineers (2018)
Using digital twins to replicate human physiology
allow us to understand and test the best potential
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Figure 14: Place North West (2017)
Alstom leverages digital twins to optimize
maintenance regimes and capacity in the UK.
https://www.placenorthwest.co.uk/news/
alstoms-widnes-train-factory-enters-servicewith-paint-job/
Figure 15: Siemens (2018)
Data sources enabling electrical grid digital twins.
https://new.siemens.com/global/en/products/
energy/energy-automation-and-smart-grid/
electrical-digital-twin.html
Figure 16: SmartCitiesWorld (2018)
A digital twin provides a rich single-paneof-glass view of an urban project. https://
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Sitename/DAM/016/Cityzenith_Amaravati1_
PR.jpg
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A visualization of the digital twin technology
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ece5/ALTERNATES/LANDSCAPE_600/
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Shell will develop digital twins of existing oil
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Electronic store labels act as critical sensors to
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Digital twins of sensitive shipments will bring
next-level visibility to the item and its packaging
during transit. https://cargo.finnair.com/en/cargonews/internet-of-things-logistics-kari-saarikoski
Figure 21: DHL (2017)
DHL uses heat maps based on internet of things
technology to optimize operational efficiency and
lay the foundations for safer working practices
in warehouses. https://www.youtube.com/
watch?v=ufmh0vrr3Fw
Figure 22: DHL (2019)
Augmented and virtual reality glasses used for
vision picking provide a valuable data stream
to leverage in warehouse digital twins. https://
www.dpdhl.com/en/media-relations/pressreleases/2019/dhl-supply-chain-deploys-latestversion-of-smart-glasses-worldwide.html
Figure 23: DHL (2019)
A visionary example of the elements involved in a
digital twin of an entire supply chain network.
Figure 24: CraneMarket
The already advanced Singapore port is about
to get a major upgrade with digital twins.
https://cranemarket.com/blog/the-worldstransshipment-hub-automates-crane-operation/
Figure 25: The Straits Times (2018)
A digital twin to test the upcoming Tuas mega port
design. https://www.straitstimes.com/singapore/
transport/digital-twin-to-test-upcoming-tuasmega-ports-design
Figure 26: DHL (2019)
Digital twins will drive greater configuration and
customization, and this will require more flexibility
and better quality control for fulfilling orders.
Figure 27: DHL (2019)
Digital twins will help optimize in-plant material
flows.
Figure 28: DHL (2019)
Companies will need sophisticated logistics
solutions to deliver support on the ground.
Figure 29: DHL (2019)
Visibility of assets, materials, and shipments will
further improve with digital twins.
Figure 30: DHL (2019)
Digital twin-enabled industrial equipment will
accelerate service logistics needs.
Figure 31: DHL
Complete visibility of the performance of
industrial products will drive new approaches to
managing these complex assets.
Pictorial Sources
Page 36/39
For more information about ‘Digital Twins
in Logistics‘, please contact:
Further Information
Ben Gesing
Senior Innovation Manager
DHL Customer Solutions and Innovation
Junkersring 55, 53844 Troisdorf,
Germany
Phone: +49 224 1120 3336
Mobile: +49 172 773 9843
e-mail: Ben.Gesing@dhl.com
Dr. Markus Kückelhaus
Vice President, Innovation and Trend
Research
DHL Customer Solutions and Innovation
Junkersring 55, 53844 Troisdorf,
Germany
Phone: +49 224 1120 3230
Mobile: +49 152 5797 0580
e-mail: Markus.Kueckelhaus@dhl.com
PUBLISHER
DHL Customer Solutions & Innovation
Represented by Matthias Heutger
Senior Vice President
Global Head of Innovation & Commercial
Development, DHL
PROJECT MANAGEMENT AND
EDITORIAL OFFICE
Ben Gesing
Klaus Dohrmann
Tanja Grauf
Dalia Topan
AUTHORS
Klaus Dohrmann
Ben Gesing
Jonathan Ward
SPECIAL THANKS TO
Janina Kugel
Eric Seidel
Professor Lee Hoo Hay
Sam George
Xavi Esplugas
... as well as our thousands of visitors to the
global DHL Innovation Centers who inspire us
every day with their use of technology.
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Recommended Reading
Please download our trend reports from
www.dhl.com/innovation
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Deutsche Post DHL
Group
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