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Digital Twin: Benefits, use cases, challenges, and opportunities


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Applications of Digital Twin technology have been growing at an exponential rate, and it is transforming the way businesses operate. In the past few years, Digital Twins leveraged vital business applications, and it is predicted that the technology will expand to more applications, use cases, and industries. The purpose of this paper is to do a literature review and explore how Digital Twins streamline intelligent automation in different industries. This paper defines the concept, highlights the evolution and development of Digital Twins, reviews its key enabling technologies, examines its trends and challenges, and explores its applications in different industries. Available at
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Decision Analytics Journal 6 (2023) 100165
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Decision Analytics Journal
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Digital Twin: Benefits, use cases, challenges, and opportunities
Mohsen Attaran a,
, Bilge Gokhan Celik b
aOperations Management, School of Business and Public Administration, California State University, Bakersfield, 9001 Stockdale
Highway, CA 93311-1099, United States of America
bSchool of Engineering Computing and Construction Management, Roger Williams University, One Old Ferry Road, Bristol, RI 02809, United States of America
Digital Twins
Digital Twin Technologies
Digital Twin Drivers
Healthcare and Life Sciences
Automotive and Aerospace Industry
Construction and Real Estate
Applications of Digital Twin technology have been growing at an exponential rate, and it is transforming the
way businesses operate. In the past few years, Digital Twins leveraged vital business applications, and it is
predicted that the technology will expand to more applications, use cases, and industries. The purpose of this
paper is to do a literature review and explore how Digital Twins streamline intelligent automation in different
industries. This paper defines the concept, highlights the evolution and development of Digital Twins, reviews
its key enabling technologies, examines its trends and challenges, and explores its applications in different
1. Introduction
Digital Twin is attracting attention from practitioners and scholars
alike. Today, the technology is used across many industries to provide
accurate virtual representations of objects and simulations of opera-
tional processes. In 2019, a Gartner survey revealed that Digital Twins
were entering mainstream use by organizations. It predicted that 75
percent of Internet of Things (IoT) organizations also use Digital Twin
technology or plan to use it by 2020 [1,2]. Gartner also estimates that
by 2027, over 40 percent of large companies worldwide will be using
Digital Twin in their projects to increase revenue [3].
Moreover, Global Market Insight estimated that the Digital Twin
market size estimated in 2022 at $8 billion is expected to grow at
around 25 percent Compound Annual Growth Rate (CAGR) from 2023
to 2032 [4]. Finally, according to another recent report by global
technology research, the Digital Twin market is set to grow by nearly
$32 billion from 2021 to 2026 [5]. In addition, according to a 2022
report, nearly 60 percent of executives across a broad spectrum of
industry plan to incorporate Digital Twins within their operations by
2028 [6].
Digital Twin is a cutting-edge technology that has revolutionized
the industry by mirroring almost every facet of a product, process,
or service. It has the potential to replicate everything in the physical
world in the digital space and provide engineers with feedback from
the virtual world [7]. As a result, the technology enables companies
to quickly detect and solve physical problems, design and build better
products, and realize value and benefits faster than previously possi-
ble. Furthermore, the Digital Twin technology enables businesses to
improve business processes and performance [8].
Corresponding author.
E-mail addresses: (M. Attaran), (B.G. Celik).
The research started by developing key concepts based on a nar-
rative literature review in the Scopus database and Google Scholar
and searched for papers that included Digital Twin or Digital Twins in
the title. Articles were selected from Journal articles, research articles,
conference publications, research reports, or scientific encyclopedias.
We reviewed the documents to find answers to the following questions:
What is a Digital Twin, what technologies are used, what are the cur-
rent applications of Digital Twins in different industries, and what are
the challenges and opportunities facing this growing technology? We
selected research papers that defined the concept, reviewed its related
technologies, and highlighted its applications and trends in different
industries. Section 2provides the main perspectives and definitions of
Digital Twins in literature. Section 3reviews four enabling technologies
of the Digital Twins. Section 4studies Digital Twin applications and
use cases of different industries. Section 5highlights the challenges and
opportunities of this technology. Finally, Section 6provides a summary
and conclusions. Section 7 provides references.
2. Background and definition
In 2003 Professor Grieves of the University of Michigan introduced
the concept of Digital Twins in a total product lifecycle management
course. It is also known as a digital mirror and digital mapping. Since
then, its definition has continued to evolve as several scholars have
provided varied definitions of this technology [914]. Encyclopedia of
Production Engineering states that ‘‘The Digital Twin is a representation
of an active unique ‘‘product’’ which can be a real device, object, machine,
service, intangible asset, or a system consisting of a product and its re-
lated services’’ [12]. In general, the Digital Twin is defined as virtual
Received 17 November 2022; Received in revised form 4 January 2023; Accepted 14 January 2023
Available online 21 January 2023
2772-6622/©2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license
M. Attaran and B.G. Celik Decision Analytics Journal 6 (2023) 100165
Fig. 1. Levels of integration.
representations of physical objects across the lifecycle that can be
understood, learned, and reasoned with real-time data or a simulation
model that acquires data from the field and triggers the operation of
physical devices [15,16].
Furthermore, Digital Twin was defined as the convergence between
physical and virtual products [17,18]. Fu et al. [7] thought of the
Digital Twin as a real-time digital representation of a physical object.
They are remotely connected to real objects and provide rich repre-
sentations of these objects. They go beyond static product designs, like
CAD models, but comprise dynamic behavior [19,20]. A virtual replica
of a real-world asset is obtained through constant data transmission
allowing the digital version of the object to exist simultaneously with
the physical one [17]. Digital Twin uses big data technology to mine
hidden and effective data and to improve the intelligence and appli-
cability of Digital Twin technology, especially for quick identification
and evaluation of design flaws [21].
Finally, Kritzinger et al. [13] defined Digital Twin, within the field
of manufacturing, based on the data integration level, which can be
achieved between the physical product and its virtual representation.
He identified three levels of integration, The Digital Model, Digital
Shadow and the Digital Twin (Fig. 1) [13,22].
A historic early application of Digital Twin technology is when
NASA engineers used a simulator, a twin of the command module, and
a separate twin of the module’s electrical system to remedy and save
Apollo 13 in 1970. NASA engineers completed the process in under two
hours and saved the lives of the three astronauts on board. This was an
extraordinary early application of this technology, and the technology
has only matured since then [23]. Today, NASA uses Digital Twins to
develop next-generation vehicles and aircraft.
The concept of Digital Twins is not new. However, Digital Twins
have moved from idea to reality much faster in recent years. It is pre-
dicted that Digital Twins will be combined with more technologies such
as speech capabilities, augmented reality, IoT, and artificial intelligence
(AI). As a result, Gartner included Digital Twins on its list of top 10
technology trends for 2017 [24]. Gartner also predicted that half of
the large industrial firms to use Digital Twins in crucial business appli-
cations by 2021 [24]. Finally, MarketsandMarkets research predicted
rapid growth for the Digital Twin technology within the next few years,
thanks to rising interest in the manufacturing industry to reduce cost
and improve supply chain operations. As a result, the market for Digital
Twin technology was valued at $6.9 billion in 2022. However, it is
expected to reach $73.5 billion by 2027- a CAGR of more than 60
percent [25].
3. Digital twin technologies
The three main aspects of Digital Twins are data acquisition, data
modeling, and data application [26]. Digital Twin uses four technolo-
gies to collect and store real-time data, obtain information to provide
valuable insights, and create a digital representation of a physical ob-
ject. These technologies include the Internet of Things (IoT), Artificial
Intelligence (AI), Extended Reality (XR), and Cloud (Fig. 2). In addition,
Digital Twin uses a particular technology, depending on the application
type, to a greater or lesser extent.
1. Internet of Things (IoT): IoT refers to a giant network of connected
‘‘things’’. The connection is between things–things, people–things,
or people–people [27]. Digital Twins use IoT as its primary
technology in every application. By 2027, more than 90 percent
of all IoT platforms will have Digital Twinning capability [6].
IoT uses sensors to collect data from real-world objects. The data
transmitted by IoT is used to create a digital duplication of a
physical object. The digital version then can then be analyzed,
manipulated, and optimized. IoT constantly updates data and
helps Digital Twin applications create a real-time virtual rep-
resentation of a physical object. Therefore, every Digital Twin
application uses IoT as a primary technology.
2. Cloud Computing: Cloud computing refers to delivering hosted
services over the Internet. The technology efficiently stores and
accesses data over the Internet [27]. Cloud computing provides
Digital Twins with data computing technology and cloud data
storage technology. Cloud computing allows Digital Twin, with
large volumes of data, to store data in the virtual Cloud and
easily access the required information from any location. Cloud
computing enables Digital Twins to effectively reduce the com-
putation time of complex systems and overcome the difficulties
of storing large amounts of data [28].
3. Artificial Intelligence (AI): As a discipline of computer science,
AI seeks to mimic the basis of intelligence to create a new
intelligent machine capable of responding like human-to-human
intelligence. Areas of AI study include Robotics, image recog-
nition, and language recognition. Neural Networks, Machine
Learning, Deep Learning, and expert systems [29], AI can assist
Digital Twins by providing an advanced analytical tool capable
of automatically analyzing obtained data and providing valu-
able insights, making predictions about outcomes, and giving
suggestions as to how to avoid potential problems [26].
4. Extended Reality (XR) is an umbrella term used to describe
immersive technologies like Virtual Reality (VR), Augmented
Reality (AR), and Mixed Reality (MR). These technologies can
merge the physical and virtual worlds and extend the reality
we experience [30]. XR creates digital representations of objects
where digital and real-world objects co-exist and interact in real-
time. Digital Twins utilize XR capabilities to digitally model
physical objects, allowing users to interact with digital content.
4. Digital twins use cases and applications
Today, engineering and manufacturing predominantly use Digital
Twins to provide accurate virtual representations of objects and simula-
tions of operational processes. Digital Twins applications in operations
and supply chain management, especially the role of Digital Twins in
terms of operations traceability, transport maintenance, remote assis-
tance, asset visualization, and design customization, are reviewed in
related publications [13,3138]. The technology is poised to deliver
upon its many promises in other industries, including automotive,
aerospace, construction, agriculture, mining, utilities, retail, health-
care, military, natural resources, and public safety sectors (Fig. 3)
[10,12,19,34,3844]. The technology has captured the imagination
of scholars, managers, and practitioners worldwide, and numerous
business applications of this technology are emerging in the literature
Fig. 4 highlights the impact of Digital Twins solutions on the busi-
ness world, as explained in the literature and discussed in detail in
Section 4.
M. Attaran and B.G. Celik Decision Analytics Journal 6 (2023) 100165
Fig. 2. Technologies of Digital Twins.
Fig. 3. Industries using Digital Twins.
4.1. Applications in manufacturing
The manufacturing industry is undergoing a rapid transformation.
As a result, interest in exploiting technologies such as Digital Twins
in the manufacturing industry is increasing. Digital Twins technology
holds great potential for a range of activities in the manufacturing
industry and can radically change the face of manufacturing [37,51].
Industry 4.0 have enabled technological advancements in sensing,
monitoring, and decision-making tools. These advancements helped the
precise implementation of Digital Twin for the real-time monitoring
and optimization of the process [35,38]. Fig. 5 highlights the significant
technological evolution of Digital Twins over the past four decades
There are many potential use cases for Digital Twinning in man-
ufacturing, including monitoring, simulation, and remote control of
physical assets with virtual objects. In addition, Digital Twin technol-
ogy can assist manufacturing in improving customer satisfaction by
better understanding their needs, developing enhancements to existing
products, operations, and services, and helping drive new business
innovation [52]. By utilizing the power of the Digital Twin, manufac-
turing companies can move from being reactive to predictive. They can
predict when equipment is wearing down or needs repair, improve the
machine’s performance, extend their lives and learn how to redesign to
do even more. Additionally, Digital Twins enable them to do usage-
based design and pre-sales analytics and add intelligence to manual
processes to enhance visibility into customer needs, etc. The following
sections discuss these capabilities and Digital Twins’ usage in more
4.1.1. Product design
The introduction of new products or services can have impacts
throughout the organization. Furthermore, product and service design
has strategic implications for the success of an organization. Conse-
quently, decisions regarding product and service design are among the
most fundamental that managers must make. A Digital Twin provides a
virtual replica of a manufacturing asset that collects data and provides
the ability to create, build, test, and validate predictive analytics and
automation [22]. Engineers can use the virtual prototype generated
by Digital Twins during the design phase to test different designs
M. Attaran and B.G. Celik Decision Analytics Journal 6 (2023) 100165
Fig. 4. Digital twins use cases and applications.
Fig. 5. Evolution of Digital Twins in manufacturing.
before investing in a solid prototype [53]. This reduces the number
of prototypes, save time, and reduces production cost. Furthermore,
engineers and designers can use data collected over time to improve
customer expectations regarding product quality, customization, and
ease of use [51].
4.1.2. Process design and optimization
Digital Twin helps manufacturers observe processes under multiple
performance conditions and eliminate problems before they occur. That
allows manufacturers to move from being reactive to predictive. In
addition, Digital Twin helps turn existing assets into tools that optimize
processes, save money and accelerate innovation [53].
4.1.3. Supply chain management
The eternal cycle of rising supply chain costs impacts all players’
bottom lines. As a result, manufacturers, retailers, and distributors
have identified supply chain cost reduction as critical. Additionally,
excellent supply chain performance has a strategic value that could
lead to rapid financial payback, often within months, and improve-
ments in productivity and profits [54]. Digital Twin technology can
solve supply chain challenges, including packaging performance, fleet
management, and route efficiency [55]. Additionally, Digital Twins can
help optimize just-in-time or just-in-sequence production and analyze
distribution routes. The technology is also helpful in other vital phases
of supply chain management, including Product inception, Product
development, and Product distribution. More specifically, Digital Twins
help to track and analyze packaging performance, fleet management,
and route efficiency [56,57].
4.1.4. Preventive maintenance
Preventive maintenance focuses on predicting when to schedule
maintenance for a component or system to reduce cost and increase
machine uptime [58]. Digital Twins can model individual equipment
or manufacturing processes to identify variances that indicate the need
for preventive repairs or maintenance. The aim is to estimate, predict,
detect, or diagnose the condition of a component or a system for
maintenance more effectively. This would prevent costly failure before
a serious problem occurs. They can also determine if better materials
or processes can be utilized or help optimize cycle times, load levels,
and tool calibration [41].
4.1.5. Cross-functional collaboration
Digital Twins are often used to collect operational data over time.
This data provides insights into product performance, distribution, and
M. Attaran and B.G. Celik Decision Analytics Journal 6 (2023) 100165
end-user experience and can be shared by engineering, production,
sales, and marketing. Employees across disciplines can all use the same
data to make more informed decisions.
4.2. Applications in agriculture
The agriculture industry is essential to the functioning of any econ-
omy. This industry is an important source of food and raw materials;
it is also a vital source of employment opportunities for the total
population. The crops, livestock, and seafood produced in the United
States, combined with food service and other agriculture-related in-
dustries, contribute more than $750 billion annually to the economy
[59]. Additionally, the world population is increasing, and market
demand for higher product quantity and quality standards is growing,
making the issue of food security, sustainability, productivity, and
profitability more important. Furthermore, the economic pressure on
the agricultural sector and environmental and climate change issues
are increasing [60].
4.2.1. Farm management, resource optimization
Farming processes are highly complex and dynamic because they
depend on natural conditions, such as weather, diseases, soil condi-
tions, seasonality, and climate [61]. Digital Twins technology has the
potential to significantly enhance the needed control capabilities of
the agricultural industry by enabling the decoupling of physical and
information aspects of farm management. The technology can give a
virtual representation of a farm with great potential for enhancing
efficiency and productivity while reducing energy usage and costs. Al-
though Digital Twin concepts in smart farming are in their infancy and
early demonstration stages, many farmers are considering integrating
intelligent technologies and techniques that enhance the efficiency of
the farming process [39].
4.2.2. Weather modeling
An agricultural Digital Twin can also help in weather modeling
and prediction of the long-term effects of climate change. Furthermore,
Digital Twins allow farmers to identify where and how the agricultural
system’s resources are stressed by factors such as soil quality, pollution,
invasive plants, animals, or other factors [62].
4.2.3. Soil management
Digital Twin can assist in measuring and understanding everything
we can about the content and capacity of the soil in which crops
grow and the seeds and crops that require that soil [62]. Using Digital
Twins, the simulated outcomes through a growing season can answer
questions about expected yield. The required fertilizer, Sunlight, Water,
etc. [43,63,64].
4.2.4. Supply chain management
In agriculture food supply chains, customers prioritize the safety of
agriculturally produced foods while farmers look for revenue increases.
However, the complexities and dynamism of food supply chains put
many obstacles to the effectiveness of traceability and management of
food products [65]. Therefore, it is critical to have complete visibility
into the farm supply chain management to guarantee the food’s quality.
Digital Twin technology provides the agriculture supply chain with
greater traceability and transparency. Furthermore, the utilization of
this technology improves the community of the various stakeholders
that can support farmers by continuously monitoring physical farms
and updating the state of the virtual world [39].
4.2.5. Livestock monitoring
Finally, Erdélyi and Jánosi [44] explored the application of Digital
Twin for monitoring, managing, and optimizing livestock. Jo et al.
[66] proposed Digital Twin technology for simulating the energy con-
sumption of a pigsty to provide decision support for optimal pigsty
design. In addition, the same researcher investigated the feasibility
of an agricultural Digital Twin for the optimal growth of agricultural
livestock, achieved through the regulation of barn systems to maintain
air quality and temperature [67].
4.3. Healthcare and life sciences
Digital Twin solutions have been widely used in manufacturing and
other industries Digital Twins’ application in life Sciences mirrors its
application in different industries. The applications of Digital Twins
technology in the healthcare industry are limitless. The COVID-19
pandemic forced the healthcare and life sciences industry to accelerate
its digital transformation efforts [4]. Digital healthcare processes need
to become more efficient to support the mass shift to the pandemic.
Like any other industry, the life sciences industry is exploring ways to
improve efficiency and reduce costs. Now, providers are under pressure
to digitally transform and adapt to increasing patient expectations. As
a result, interest in exploiting technologies such as Digital Twins in the
life sciences is increasing [6].
The technology is increasingly finding use in life sciences applica-
tions, especially drug discovery and development [68] For example, an
experimental Digital Twin of the human heart comes from a software
company ’Dassault.’ The company software turns a 2D scan of a human
into an accurate full-dimensional model of an individual’s heart called
"Living Heart’’. This realistic human organ model accounts for blood
flow, mechanics, and electricity. The Living Heart model is now being
used worldwide to create new ways to design and test new devices and
drug treatments [69].
Furthermore, Digital Twin solutions are utilized to create repli-
cas and digital models of patients, healthcare facilities, and medical
devices. The objectives are to monitor, analyze, and predict issues
like personalizing care delivery, predictive maintenance of healthcare
facilities, and increasing R&D costs [70].
4.3.1. Drug development
We can use the Digital Twin technologies to test new drugs to ascer-
tain drug safety and effectiveness. Each step of the drug development
process produces enormous amounts of data that must be managed.
Digital Twins use those data to create a model [71]. As a result, Digital
Twins can speed up clinical trials in drug research allowing clinical
trials to run more quickly, with fewer patients needing to be assigned to
receive a drug [72]. Furthermore, Digital Twins will play a key role in
developing and producing new vaccines by helping scientists select the
best antigen to use, where the development process can also be done
4.3.2. Advanced diagnosis and preventive treatment
Digital Twin technology can simulate various patient characteristics
and replicate how they behave and respond in specific situations. This
information could help track their health, diagnose diseases, and plan
preventive treatments. Additionally, the data obtained from simulation
can provide a representative view of a drug’s impact on a broader
population [7173].
4.3.3. Clinical research
Digital twin technology has the potential to revolutionize the meth-
odology of clinical research. Digital Twins can be used to predict the
impact of experimental treatments on a patient, get better answers, and
derive actionable insights without risking their life. In addition, the
technology helps healthcare professionals study the infected patient’s
M. Attaran and B.G. Celik Decision Analytics Journal 6 (2023) 100165
data for future research in performing treatment simulations and identi-
fying the most promising paths for further research among real people.
[4]. For example, a Swedish University has created a Digital Twin of
mice affected by rheumatoid arthritis to understand drug efficacy and
create a replacement for clinical trials on human beings [70].
4.3.4. Personalized medicine
Digital Twin solutions are more accessible and are transforming how
the healthcare industry transforms the lives of the people they serve.
For example, Digital Twin solutions can be used to fulfill the promise of
personalized medicine worldwide [73]. The technology enables physi-
cians to leverage potentially thousands of variables and digital care-
backed clinical decision-support solutions to model a patient’s best
course of treatment intelligently. Additionally, Digital Twin solutions
help to study diseases such as Multiple Sclerosis and Alzheimer’s to
better understand treatment options and reduce trial timelines. Finally,
Digital Twin technology can create simulations of new treatments
and bring lifesaving innovations to market more quickly. Oklahoma
State University’s researchers, for example, experimented with a Digital
Twin of the respiratory system and simulated aerosol drug particle
movements for its delivery in improving lung cancer therapy [70].
Likewise, Siemens developed a Digital Twin of the human heart using
millions of images and reports to facilitate an in-depth understanding
of heart conditions and predict illness or any underlying health issues.
Similarly, a French company developed a Digital Twin of the aneurysm
and surrounding blood vessels to enable surgeons to select various
patient operating devices based on the Digital Twin study [70].
4.3.5. Facility and operations design
As hospitals struggle to lower operating costs and remain compet-
itive, Digital Twin solutions have the potential to reduce costs and
improve a patient’s journey through a medical facility. Digital Twins
can assist healthcare organizations in optimizing hospitals, capacity
planning, workflows, staffing systems, and care delivery systems. For
example, Digital Twins can mitigate the impact of inaccurate bed
occupancy predictions through better predictive analytics based on
combining internal and external data—tracking patient flow internally
and forecasting potential spikes using external data. Digital Twin,
Healthcare providers can use the information provided by Digital Twin
solutions to think more strategically about capacity and resources based
on better forecasting—improving patient care, operational efficiency,
and profitability. Additionally, Digital Twins can be used to model
personalized and intelligent medical devices and other equipment to
improve efficiency and optimize costs (El Saddik, 2018; [50]). Digital
Twins use information gathered by IoT sensors embedded in the device
to collect information about the configuration and maintenance history
of the device.
4.3.6. Education and training
VR technology has been used for decades in medical training, sup-
plementing and enhancing traditional health education. The technology
also helped doctors treat patients and surgeons perform robotic surgery.
Artificial Intelligence (AI) enabled Digital Twins, using the power of
augmented and virtual reality, can facilitate healthcare practitioners’
training and education needs. For example, numerous companies have
simulated medical anatomy and surgical procedures to inspire interac-
tive learning and minimize the usage of cadavers [48]. Medical schools
can use Digital Twin solutions to teach surgical techniques in simulated
formats [73].
4.3.7. Diagnosis and therapy
Researchers are exploring the use of Digital Twins for advanced un-
derstanding of the human body by generating Digital Twins of a single
cell, genome, or organs. Digital Twins allow researchers to track pa-
tients’ health, diagnose diseases, and plan preventive treatments [68].
For example, surgeons can use the virtual digital replica of a patient
to plan the surgical procedure and identify the risks associated with
the surgery or help mitigate the need for surgery altogether [48,70].
One application of this solution is the National Institute of Health (NIH)
Digital Twin models to predict athletes’ concussion-related trauma from
brain injuries. The data provided by this solution accelerate surgical
procedures and speed up recovery plans sim
4.4. Automotive & aviation industry
Intense competition among manufacturers for introducing advanced
and innovative cars is encouraging companies to invest in the R&D of
products and automation of processes. Several automobile manufactur-
ers are adopting upcoming technologies like Digital Twins using
interactive automobile dashboards on websites to improve customer
engagement. Customers can customize vehicles at their convenience.
Companies use the information to monitor consumer behavior and
change existing models [4]. Digital Twin technology is becoming a
global area of research where researchers cover Digital Twin imple-
mentation on various aspects of intelligent vehicles and explore its
potential, opportunities, and challenges to the realization [42].
Digital Twin technology is also widely embraced in the aerospace
industry. It is used for aircraft maintenance, tracking, weight moni-
toring, an accurate stipulation of weather conditions, measurement of
flight time, and defect detection [74]. For example, Boeing, the world’s
largest aerospace company, uses Digital Twin solutions to improve
the quality and safety of the parts and systems used to manufacture
commercial and military airplanes. As a result, Boeing claims to have
achieved a 40 percent improvement in the quality of their airplane
parts and systems [75].
4.5. Construction and real estate
Using Digital Twins as virtual replicas of physical assets in the
construction and real estate industries can revolutionize managing
assets and projects. Digital Twins as virtual models of a physical asset
have similarities to Building Information Modeling (BIM), which has
been used by building industry professionals for many years. Building
Information Modeling (BIM) is the digital representation of the physical
and functional characteristics of a building or construction project
[76]. It provides a shared knowledge resource for information about a
building or project, including geometric descriptions, spatial relation-
ships, geographic information, quantities, and properties of building
components [77]. While BIM provides static data, Digital Twins, using
sensors, provides real-time data that construction managers, designers,
or their clients can use to track projects in real-time [78]. Using
Digital Twins, construction teams can monitor the construction process,
identify potential problems, and adjust strategies to ensure that projects
are completed safely, on time, and within budget at the agreed-upon
quality. Furthermore, Digital Twin solutions in the construction indus-
try can help track other resources (i.e., materials, labor, equipment),
monitor safety, and conduct resource planning and logistics [79].
Digital Twins can provide a comprehensive overview of the physical
asset in the real estate industry, allowing agents or property owners
to collect and analyze data related to the asset’s performance and
In short, using Digital Twins in construction and real estate has the
potential to drive unprecedented efficiency in cost, time, sustainabil-
ity, and safety, making it an invaluable tool for the overall building
4.5.1. Automate project control
Digital Twins can help with construction progress tracking by pro-
viding real-time insights into the status and performance of construc-
tion projects. This can be achieved by integrating data into a virtual
model from various sources, such as sensors, drones, laser scanners,
and other monitoring systems [80]. Digital Twin solutions powered by
M. Attaran and B.G. Celik Decision Analytics Journal 6 (2023) 100165
AI can process, analyze, and present the integrated data as an as-built
model with daily or hourly comparisons to the baseline model. Such
tracking would help solve common construction progress problems by
detecting any early deviations from the budget or schedule, allowing
project teams to develop and implement any necessary recovery plans.
4.5.2. Resource planning and logistics
In the construction industry, unnecessary movement and handling
of materials, equipment, and labor can be significant sources of waste.
Digital twin solutions can help reduce this waste by enabling a lean
approach to resource management. These solutions can provide real-
time monitoring of resource allocation and waste tracking, improving
the efficiency and productivity of the construction process. One exam-
ple of the use of Digital Twins for construction progress tracking is the
integration of sensor data from construction equipment and vehicles.
This can provide real-time information on the location and usage of the
assets and help identify potential bottlenecks or inefficiencies. These
benefits of Digital Twins would help the construction industry avoid the
over-allocation of resources and improve time management [8184].
4.5.3. Construction safety
The safety record of the construction industry is not good. Digi-
tal Twins technology combines AI with video cameras, sensors, and
mobile devices to build an extensive safety net for the construction
workplace [84]. The construction industry can use the real-time site
reconstruction feature Digital Twins offer to track and monitor the
construction process in real-time, helping to identify and address any
issues or deviations from the plan that may pose a safety risk [29]. This
information can help prevent the usage of unsafe materials and activity
in dangerous zones. In addition, management can create a system of
early notification where they are notified if a worker is in danger and
send a message to a worker’s wearable device [84]. Additionally, upon
identifying unsafe behaviors, Digital Twins can be used to provide
targeted training for workers in virtual environments, reducing the
need for costly and time-consuming physical training and ultimately
helping minimize the risk of accidents [29].
4.5.4. Quality control and assessment
Digital twin solutions can help with construction quality control and
assessment by providing real-time insights into the performance and
behavior of the different components and systems of the construction
project. Digital twin technology can use image processing algorithms to
analyze video or photographic images of a construction site to assess
the condition of various components and materials. For example, it can
be used to check the concrete condition or identify cracks in columns
or other structures. This can help with the detection of potential issues
or defects and with the implementation of corrective actions to ensure
the quality of the construction process [84].
4.5.5. Building performance assessment
Digital twin solutions can be used to assess building performance by
simulating the behavior and performance of the building under differ-
ent conditions and scenarios. This can be achieved by integrating data
from various sources, such as sensors, simulations, and BIM models. For
example, Digital Twins can be used to analyze the energy efficiency of
a building by simulating the consumption and generation of energy and
by evaluating the impact of different design and operational strategies.
They can also be used to assess the comfort and indoor environmental
quality of a building by stimulating the flow of air, heat, and humidity
and by evaluating the impact of different design and control strategies
4.6. Utilities
The Digital Twin solutions rapidly integrate into the traditional
manufacturing industry, smart city, and intelligent power grid [86].
Rapid developments in connectivity through IoT make the potential for
Digital Twins dramatically effective within a smart city [87]. The in-
creasing complexity of current power systems makes the digitalization
of power assets one of the most discussed topics in the energy sector.
Digital Twin technology can be a helpful tool for asset optimization.
It can be applied across the whole energy sector to achieve optimal
results in terms of maintenance, production planning, plant efficiency,
and risk mitigation. A recent study proposed a digital power grid based
on Digital Twin technology that can digitize the whole process, all
elements, and all services of the physical power grid, such as human
and physical events [88]. The digital power grid solution can also
help the power grid planning, design, construction, management, and
service process [89]. Therefore, it significantly impacts the efficiency
improvement of power grid energy resources and information resource
allocation [88].
4.7. Mining
For the mining industry, several challenges must be addressed if the
industry is to maintain a healthy profitability position in the future.
The main challenge is to align an environmentally aggressive opera-
tion with environmental sustainability issues, energy transition, and
greater efficiency in using natural resources. The competitive nature
of the industry has been forcing mining companies to optimize the pro-
cess and improve equipment, productivity, adaptability, and efficiency.
In addition, mining companies must embrace the newest technology
trends, including Digital Twins, to stay competitive and keep thriving
in business [90].
The Digital Twins solution is beneficial for planning schedules and
operations in the mining industry. Simulation of the work environment
enables miners to create long-term and short-term programs. Addi-
tionally, they can make accurate estimates for drilling, crushing, and
extraction. Moreover, on-site workers can use Digital Twins solutions
to simulate the equipment, machinery, and the entire work process
and will be able to test new methodologies on their most crucial work
processes. Every test will be executed in a digital simulation in a very
cost-effective manner [90].
Digital Twin technology can also enhance the training programs. It
allows on-site workers to build a digital training program to help new
interns learn the ins and outs of the mining industry, the work they will
be executing, and what future possible scenarios they can expect [90].
There are several examples of Digital Twin utilizations in the mining
industry. For instance, Rio Tinto has developed a Digital Twin system
for its Gudai-Darri iron ore operations, with a value of $2.6 billion.
This system allows field personnel and remote operations center staff
to access the same real-time data and make informed decisions within
seconds rather than waiting for hours or days [91]. Using their Digital
Twin system, Rio Tinto (n.d.) has discovered that they can test ways to
increase production without jeopardizing equipment or operations.
5. Digital twins drivers & challenges
The COVID-19 pandemic fueled the growth of Digital Twin market
size across various applications, including real estate, healthcare, en-
ergy, and retail, driving the market’s growth prospects. As such, several
countries are expected to implement Digital Twin solutions as a part
of their economic reform activities [92]. Likewise, to recover from
economic disruptions caused by the pandemic, several organizations
are also adopting Digital Twin technology to optimize their supply
chains and operational processes [5].
The current acceleration is mainly made possible by the decreasing
costs of technologies that enhance both IoT and the Digital Twin. Fur-
thermore, in the past few years, Digital Twins leveraged vital business
applications, and it is predicted that the technology will expand to
more use cases, applications, and industries. As a result, applications
of Digital Twin technologies have been growing exponentially [39].
M. Attaran and B.G. Celik Decision Analytics Journal 6 (2023) 100165
Furthermore, cloud companies like Google Cloud and Microsoft
Azure are launching cloud-based Digital Twin platforms for easy ac-
cessibility and customized solutions. For example, in January 2022,
Google Cloud introduced a supply chain Digital Twin solution to pro-
vide the manufacturing industry with visibility of operations within
their supply chains [92].
The emergence of Industry 4.0 and IoT has also accelerated the
adoption of Digital Twin technology across various applications [92].
Industry 4.0 uses innovative production methodologies and advanced
technologies, such as cloud computing, IoT, analytics, Digital Twin,
digital scanning, 3D printing, and AI. Digital Twin technology is central
to the Industry 4.0 initiatives. More and more industries are actively
using Digital Twin solutions for asset and product lifecycle management
[5]. The technology allows companies to create a virtual replica of
their products and processes and empowers them to make the necessary
decisions in advance.
As discussed in this article, Digital Twins technology has many
advantages; however, the technology currently faces shared challenges
in parallel with AI and IoT technologies. Those include data stan-
dardization, data management, and data security, as well as barriers
to its implementation and legacy system transformation [5]. Other
challenges listed in the literature include the need for updating old
IT infrastructure, the challenges of connectivity, privacy, and security
of sensitive data, and the lack of a standardized modeling approach
[10], The significant challenges likely to hamper the growth of the
Digital Twin market include the high cost of deployment, increased
demand for power and storage, integration challenges with existing
systems or proprietary software, and complexity of its architecture.
Implementing Digital Twins solutions is costly, requiring significant
investment in technology platforms (sensors, software), infrastructure
development, maintenance, data quality control, and security solutions.
Furthermore, maintaining the Digital Twin infrastructure can be costly,
requiring significant investment in operations. The high fixed cost and
the complex infrastructure of Digital Twins are expected to slow down
the deployment of Digital Twin technologies [5].
6. Summary and conclusions
In recent years, Digital Twin technology has garnered significant at-
tention from both industry and academia. There are various definitions
for this technology in the literature, as the term is applied to different
focus areas within different disciplines. The concept of a Digital Twin
can be described as the seamless data integration between a physical
and virtual machine in both directions. The Digital Twin technology
was first used in the fields of astronautics and aerospace by NASA for
the moon exploration mission Apollo 13 and Mars Rover Curiosity. The
literature review shows that the breadth and impact of Digital Twins
continue to expand, making it a fast-growing IT solution in various
As presented in this paper, most of the manuscripts published in
academic journals discuss the application of Digital Twin solutions
in manufacturing, particularly within the context of Industry 4.0. Re-
search concerning Digital Twin solutions in manufacturing deals with
production planning and control, which plays a central role in inte-
grating all data within a production system. Supply chain management
is another area where use cases of Digital Twins are reviewed in the
literature. Use cases in the construction and healthcare industry are
also growing. In the construction industry, the Digital Twin concept
and mobile devices and wearables on a construction site can help better
represent the as-built vs. the as-designed project at any given time.
In addition, it helps decrease the number of errors and reworks by
allowing up-to-date information to be fed back to the field. Digital Twin
solutions help the healthcare industry discover undeveloped illnesses,
experiment with treatments, and improve surgery preparation. Captur-
ing an accurate full-dimensional human body model will help doctors
discover undeveloped illnesses, experiment with treatments, and im-
prove surgery preparation. Researchers are working on developing
Digital Twins to analyze the human body, and significant progressions
have been made. Living Heart project is a common technology for
clinical diagnosis, testing, medical device design, and education and
training. Digital Twin solutions created the first realistic virtual model
of a human organ accounting for blood flow, mechanics, and electricity.
Advances in AI, IoT, and cloud computing and the relative strength
of these technologies created a groundwork for Digital Twin solutions
to evolve quickly and find applications in manufacturing, supply chain,
life sciences, agriculture, energy, etc. Artificial Intelligence (AI) en-
abled Digital Twins to simulate a complex real-world system. It taps
into data gathered by IoT devices to learn and run alongside real-
world manufacturing systems, continuously identifying improvement
areas and supporting tactical decision-making. It also helps optimize
systems design to increase efficiency and avoid costly redesign during
The growing demand for automation in various industries are the
anticipated factors to trigger the high demand for the Digital Twin
platform over the forecast period. As we recover from the pandemic,
Digital Twin solutions are poised to play an increasingly important role
in different industries. The benefits of creating a Digital Twin solution
are too vast and still not fully explored. While there are challenges
to addressing data quality and security, increased demand for power
and storage, and integration with existing infrastructures, Digital Twin
solutions are thriving to provide a highly advanced digital revolution
to make the world a better place for humankind. In the future, Digital
Twins will expand to more use cases and industries. The solutions will
combine with more technologies, such as augmented reality (AR), for
an immersive experience and AI capabilities for better connections,
insights, and analytics. In addition, more technologies enable us to use
Digital Twin solutions, removing the need to check the ‘real’ thing.
These exponentially higher insights and analytics, in turn, lead to even
more possibilities for applications of Digital Twin solutions in complex
Declaration of competing interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
No data was used for the research described in the article.
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... This untapped potential exists even though the digital twin is becoming increasingly important in industrial discussions to reduce cost and increase economic effectiveness in various industries. Nevertheless, the digital twin still faces several challenges, and issues such as data standardisation, data management, and data security need to be further explored and improved to realise its market potential [1]. ...
... Literature Digital twin in manufacturing [1][2][3][4][5][6][7][8] Virtual commissioning [9][10][11][12][13][14] Human-robot interaction [15][16][17][18] Line balancing of HRI assembly lines [19][20][21][22] Bin-picking [23][24][25][26][27][28] After giving an overview of the relevant literature, the following section deals with various research potentials that have been elaborated based on the literature. In particular, the intersections between the three domains production systems, HRI, and virtual commissioning are addressed. ...
... Nevertheless, the current dominant application area is engineering and manufacturing. In this context, the digital twin is mainly used for the virtual representation of objects and processes and for the simulation of operational processes [1]. ...
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Increasing volatility in manufacturing and rising sustainability requirements demand more efficient processes in production, especially in employee qualification and engineering during development and on-site adjustments before and after the start of production. One possible solution is using digital twins for virtual commissioning, which can speed up engineering processes, qualify employees, and save valuable resources. To solve these challenges, it is necessary to identify promising approaches for using the digital twin and virtual commissioning. Furthermore, creating an environment where these approaches can be optimally explored is essential. This paper presents promising research approaches and demonstrates the development of an assembly process and a production system with a digital twin designed to explore these aspects. The presented system is an interlinked production system for assembling an actual industrial product. It includes different levels of human–robot interaction and automation, which can be implemented virtually in the digital twin.
... Los DTs son tecnologías que proporcionan ventajas competitivas a las empresas. Al ser tecnologías diferenciadoras, los DTs se aplican en campos industriales, como la aeronáutica y automotriz, en la construcción, minería y salud, en la Industria manufacturera en general, en la Agricultura y Acuacultura, en la Educación y en el reacondicionamiento tecnológico (Retrofitting), entre otras [33]. La Figura 5 muestra algunas aplicaciones de los DTs. ...
... Aplicaciones de los DT, tomado y mejorado de[33] ...
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Resumen La Industria 4.0 es un nuevo paradigma industrial que busca mejorar y optimizar los procesos y la maquinaria concebidos en la Manufactura 3.0. Para lograr sus objetivos, la Industria 4.0 utiliza diversas tecnologías disruptivas (Cómputo en la Nube, Internet de las cosas, Robótica Colaborativa, Simulación, etc.), los gemelos digitales y Sistemas Ciberfísicos. Un gemelo digital es una réplica de un activo físico (servicios, procesos, objeto y sistemas) y ambos están interrelacionados entre sí, comparten información bruta y evaluada, y se actualizan en tiempo real. La simulación computacional, la ingeniería mecánica y la mecatrónica son la base de los gemelos digitales. Las aplicaciones de los gemelos digitales se han extendido a campos como la agricultura, industria automotriz, medicina y aeronáutica, entre otras, y estos representan la tecnología innovadora de las Industria 4.0 y 5.0. En este artículo se exponen las generalidades de la tecnología de los gemelos digitales, se describen algunas de sus aplicaciones y se discuten algunos aspectos de estas tecnologías en la enseñanza de la mecatrónica. Palabras clave: Industria 4.0, gemelos digitales, enseñanza de la mecatrónica. 1. Introducción Una revolución industrial es una transformación de las prácticas industriales tradicionales en nuevas técnicas dominadas por las tecnologías disponibles en aquel momento [1]. Hoy en día, el mundo industrial está experimentando una nueva revolución industrial que está transformando de forma y fondo los procesos productivos de las empresas y que presiona a las universidades ha impulsar cambios en la educación en general. Esta nueva revolución industrial tiene la capacidad de modificar y transformar aquellas economías nacientes hacia un futuro prometedor ya que sus nuevas tecnologías pueden incrementar la productividad e impulsar la innovación en diversas empresas e industrias. De hecho, la innovación tecnológica que promueve la cuarta revolución industrial a través de la aplicación de una diversidad de tecnologías disruptivas, provocará cambios importantes en toda la sociedad [2]. La cuarta revolución industrial está representada por el término Industria 4.0 (I.40) y su centro tecnológico son los sistemas ciberfísicos. La visión de la I.40 propone que la conexión en tiempo real de los sistemas físicos y digitales, junto con las nuevas tecnologías facilitadoras, cambiarán la forma en que se realiza el trabajo y, en ese sentido, será posible cambiar los equilibrios tradicionales de las operaciones que existen entre las prioridades competitivas de los costos, la flexibilidad, la velocidad y la calidad [3]. La introducción de las tecnologías disruptivas y la digitalización a los procesos industriales han provocado una aceleración en la transición de la fábrica clásica, concebida bajo la filosofía de la Industria 3.0, a la fabricación inteligente que opera bajo los objetivos y la visión de la I4.0 [4]. Esta transición es conocida como Industria 3.5 y se presenta como una estrategia híbrida entre la actual Industria 3.0 y la futura I4.0 para la migración por fases a la fabricación inteligente y la producción ecológica con las
... Among others, the huge potential of digital twins is seen in supporting safety assurance at run-time, exploring problem spaces to find optimal or near optimal solutions, foster prediction of run-time properties [4,3]. One of the primary challenges encountered with digital twins is its systematic definition, particularly considering generalization and managing large-scale operations [5,6]. A digital twin doesn't achieve immediate perfection; it necessitates the accumulation of extensive data sets before it can autonomously make accurate predictions. ...
Conference Paper
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Goal modeling is commonly proposed and used for early requirements engineering, allowing easy documentation of high-level requirements and their interrelations. In recent years, goal models have often been suggested to support the definition and analysis of cyber-physical systems, also from the industry automation domain. For the future of manufacturing, digital twins play a vital role. The digital twin allows for in-depth analysis of the requirements and processes of the factory and supports action planning for new tasks. In this paper, we investigate the use of the goal-oriented requirement language (GRL) to define goal models that support the digital twin in industry automation.
... Digital twin (DT) is an emerging technology that has attracted significant interests in many industrial applications, including power systems domain [6]- [8]. The concept of DT was summarized by Michael Grieves as a virtual representation of a physical component/system [9] after its first practice in aerospace [10]. At the initial stage of development in the last century, the original prototype of DT is considered to contain three main parts, i.e. the physical entity in real world, the virtual replica in virtual space, and the connections between them [9]. ...
Conference Paper
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This paper analyzes the requirements for the reporting rate of the live data source to support the operation of Digital Twins (DTs) of Distributed Energy Resources (DERs) for real-time power systems monitoring applications. The visibility of distribution networks is currently limited due to the lack of sufficient measurement and communication infrastructures. With the rapid increase of DERs, it becomes increasingly important to improve the visibility of the distribution networks to ensure the critical system operating constrains are continuously met. DTs are virtual replicas of physical systems, and with certain live measurement data, they can be used to accurately represent the real-time dynamics of the physical entities. The features of DTs could therefore be applied to increase the visibility of network and potentially support real-time decisions making. This paper presents the investigation of the impact of data reporting rate on DT accuracy, based on which, the paper presents a method that could be used to quantify the minimum requirements for data reporting rate to adequately support the DT operation, which provides valuable learning for specifying measurement devices and communication networks to enable DTs-based solutions.
... The large volume of data generated by IoT sensors and devices requires sophisticated analysis to extract valuable insights. Big data analytics can be used to identify patterns and trends, and to make predictions about the future performance of assets [13]. ...
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The Fourth Industrial Revolution, or Industry 4.0, is transforming the asset management landscape, presenting new challenges and opportunities. The increasing interconnectedness of assets and systems, combined with the growing availability of data, has led to the development of Industry 4.0 platforms for asset management. These platforms provide unprecedented levels of visibility and control, enabling asset managers to optimize performance and reduce costs. However, interoperability and cyber security challenges must be addressed to fully realize the benefits of Industry 4.0 asset management. In this paper, we review the state of the art in Industry 4.0 asset management, and discuss the challenges and opportunities presented by interoperability and cyber security. We propose a framework for addressing these challenges and highlight key research directions for advancing Industry 4.0 asset management. The paper concludes with a discussion of the implications of Industry 4.0 asset management for asset intensive industries, and the role of standards in ensuring interoperability and cyber security.
... With the advent of 5G, AI, Virtual Reality (VR), and Augmented Reality (AR) technologies, the sports industry has also been greatly enhanced by digitalization. These technologies serve multiple purposes, including sports analysis and the development of new services, and this trend is expected to surge in the coming years [3]. ...
Conference Paper
In recent years, information technology has become an important element in the pursuit of a more prosperous society, and this extends to the realm of sports. The digitalization is evident across various sectors, including sports. Within the context of baseball, the introduction of automated technology for umpiring is garnering significant attention. On the other hand, the frequent misjudgments by human referees have become a contentious issue. Both Major League Baseball (MLB) and Nippon Professional Baseball (NPB) have introduced a challenge system that requires replay verification in instances where there's disagreement with an umpire's decision. However, this system is not feasible in venues lacking the necessary facilities. A consequential shortage of umpires intensifies their workload, potentially leading to more misjudgments. This paper proposes an architecture for an umpire assistance system designed to address these challenges in baseball games. Our proposed system architecture facilitates the filming of a baseball game from multiple angles, rendering the challenge system independent of the venue's infrastructure. Moreover, the system autonomously makes judgments using footage from multiple cameras, thereby supporting both human umpires and automated officiating. Looking ahead, we also discuss the potential advancements when integrating this with digital twins and explore its applicability to other sports.
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Agriculture is an industry that is essential to the functioning of any economy. Our entire economic structure revolves around this one factor. Agriculture is not only an important source of food and raw materials, but it is also an important source of employment opportunities for a significant fraction of the total population. In agriculture food supply chains, also known as ASCs, customers are the ones who pay for the agricultural products that farmers produce. During this process, customers place a higher priority on the safety of agriculturally produced foods, while farmers anticipate an increase in their revenues. The effectiveness of traceability and management for agri-food products is met with a vast array of obstacles as a result of the complexities and dynamism of ASCs. However, the majority of the currently available solutions are not able to satisfactorily meet the standards of ASCs regarding traceability and management. Chen et al., proposed a blockchain-based ASC framework to provide product traceability. This framework guarantees decentralized security for the agriculture food tracing data stored in ASCs. This was done in order to address the problems that have been presented. In order to guarantee the quality of the food, it is critical to have complete visibility into the agricultural supply chain management. Along with that, it improves the overall pleasure of the customers and the productivity of the peers. The centralized storage of data makes it more difficult to guarantee the items' quality and rate, as well as their point of origin. Consequently, we are in need of a decentralized system that allows for transparency and provides everyone, from the producers to the consumers, with the satisfaction they seek. The supply chain can now have both greater traceability and transparency thanks to blockchain technology, which is a form of digital technology. According to Madumidha utilization of this technology results in an improvement to the community that exists between the various stakeholders and the farmers. Increased capacity, improved security, immutability, minting, speedier settlement, and full traceability of all recorded transaction records are fundamentally provided by blockchain's features.
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Smart manufacturing becomes a major trend of manufacturing industry in the context of Industry 4.0. The integration of physical manufacturing machines and digitized virtual counterparts is promoted by emerging concepts and technologies of Digital Twin. Aiming to seamlessly integrate the cyberspace and physical world by constructing a bi-directional mapping system, Digital Twin can highly improve the user experience and production efficiency in smart manufacturing. So far little attention has been paid to the mapping from the cyberspace to the physical world in Digital Twin. Without this mapping, operations on the digitized virtual machines are incapable of working on the physical ones, which actually limits the applicability of Digital Twin to more manufacturing processes. In addition, the traditional 2D interactive interface in the cyberspace is limited in visualizing the large amount of digital data and providing concise information to improve the operation efficiency. To optimize the conventional Digital Twin mapping system, this paper proposes a modular-based Digital Twin system for smart manufacturing, where the bi-directional real-time mapping of the cyber–physical space is established through socket communication. Moreover, the proposed Digital Twin system aggregates the functions of remote control and virtual machining using virtual reality and augmented reality. These two essential functions are designed to provide an immersive and friendly operating environment as well as a vivid preview of machining outcomes to improve production efficiency, minimize machining cost, and avoid potential risks. The feasibility and effectiveness of the proposed Digital Twin system are demonstrated by implementing the system on a CNC milling machine where the control latency and virtual machining accuracy are verified. The proposed Digital Twin system can be utilized as an essential part of smart manufacturing, having high potential to be applied to various industrial machines and smart systems.
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The digital transformation process fostered by the development of Industry 4.0 technologies has largely affected the health sector, increasing diagnostic capabilities and improving drug effectiveness and treatment delivery. The Digital Twin (DT) technology, based on the virtualization of physical assets/processes and on a bidirectional communication between the digital and physical space for data exchange, is considered a game changer in modern health systems. Digital Twin applications in healthcare are various, ranging from virtualization of hospitals' physical spaces/organizational processes to individuals' physiological/genetic/lifestyle characteristics replication, and include the modeling of public health-related processes for monitoring, optimization and planning purposes. In this paper, motivated by the current COVID-19 pandemic, we focus on the application of the Digital Twin technology for virus containment on the workplace through social distancing. The contribution of this paper is three-fold: i) we review the existing literature on the adoption of the Digital Twin technology in the healthcare domain, and propose a classification of DT applications into four categories; ii) we propose a generalized Digital Twin architecture that can be used as reference to identify the main functional components of a Digital Twin system; iii) we present CanTwin, a real-life industrial case study developed by Hitachi and representing the Digital Twin of a canteen service serving 1100 workers, set up for social distancing monitoring, queue inspection, people counting and tracking, table occupancy supervision.
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As a highly secure and reliable system, aviation faces challenges including digital transformation, high production operation costs, low maintenance efficiency, and intensive technology. One of the most beneficial technologies is the digital twin (DT), which can solve the above issues. This paper investigates aviation industry DT from government authorities, industry, and academia. Firstly, it surveys the development of the definition of DT and compares its concepts with the Internet of Things, cyber-physical systems, and digital thread etc. Next, it shows the research timeline of aviation DT in the past 10 years by authorities such as Air Force Research Laboratory and National Aeronautics and Space Administration. Then, this paper reviews the state-of-the-art research status of DT in the whole lifecycle of the aviation system and concludes that aviation DT is the most widely used in manufacturing and maintenance, and should pay attention to the application of UAV DT. Finally, it summarizes that data fusion, high-fidelity modeling, integration with New IT, and human–machine interaction are the key technologies of aviation DT. The future possible research directions could be digital process twin risk control, interactive DT, and DT data learning.
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Context : Predictive maintenance is a technique for creating a more sustainable, safe, and profitable industry. One of the key challenges for creating predictive maintenance systems is the lack of failure data, as the machine is frequently repaired before failure. Digital Twins provide a real-time representation of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use. Since 2018, scientific literature on the utilization of Digital Twins for predictive maintenance has accelerated, indicating the need for a thorough review. Objective : This research aims to gather and synthesize the studies that focus on predictive maintenance using Digital Twins to pave the way for further research. Method : A systematic literature review (SLR) using an active learning tool is conducted on published primary studies on predictive maintenance using Digital Twins, in which 42 primary studies have been analyzed. Results : This SLR identifies several aspects of predictive maintenance using Digital Twins, including the objectives, application domains, Digital Twin platforms, Digital Twin representation types, approaches, abstraction levels, design patterns, communication protocols, twinning parameters, and challenges and solution directions. These results contribute to a Software Engineering approach for developing predictive maintenance using Digital Twins in academics and the industry. Conclusion : This study is the first SLR in predictive maintenance using Digital Twins. We answer key questions for designing a successful predictive maintenance model leveraging Digital Twins. We found that to this day, computational burden, data variety, and complexity of models, assets, or components are the key challenges in designing these models.
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Traditional design, manufacturing and maintenance are run and managed independently under their own rules and regulations in an increasingly time-and-cost ineffective manner. A unified platform for efficient and intelligent design-manufacturing-maintenance of mechanical equipment and systems is highly needed in this rapidly digitized world. In this work, the definition of digital twin and its research progress and associated challenges in the design, manufacturing and maintenance of engineering components and equipment were thoroughly reviewed. It is indicated that digital twin concept and associated technology provide a feasible solution for the integration of design-manufacturing-maintenance as it has behaved in the entire lifecycle of products. For this aim, a framework for information-physical combination, in which a more accurate design, a defect-free manufacturing, a more intelligent maintenance, and a more advanced sensing technology, is prospected.
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The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, jour-nal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.
Under the background of the 4th industrial revolution, digital twin technology has been constantly integrated with the power grid business. Power grid will build a digital twin grid covering the whole physical grid and production process. This paper first proposes the digital power grid based on digital twin. A closed-loop power grid data empowerment system is introduced to define the digital power grid based on digital twin, the three features and properties are described to enrich the digital power grid connotation. It constructs the digital power grid architecture including physical power grid, perception layer, transport layer, data layer, platform layer and application layer. The key technologies are also discussed to embody digital power grid.
The Digital Twin enables the distinctions between state sensing, entity understanding and physical automation to be eliminated, through high-fidelity modelling and bi-directional data streams. The concept of real-time virtual representation places the Digital Twin in a unique position to enable digitization in agriculture. The union of data, modelling and what-if simulation can provide an approach to overcome current limitations in decision-making support and automation, across a diverse range of agricultural enterprises. This paper conducts a Systematic Literature Review of Digital Twins in agriculture, identifying current trends and open questions with the goal of increasing awareness and understanding of the Digital Twin and its possibilities.
Advanced computer technologies such as big data, Artificial Intelligence (AI), cloud computing, digital twins, and edge computing have been applied in various fields as digitalization has progressed. To study the status of the application of digital twins in the combination with AI, this paper classifies the applications and prospects of AI in digital twins by studying the research results of the current published literature. We discuss the application status of digital twins in the four areas of aerospace, intelligent manufacturing in production workshops, unmanned vehicles, and smart city transportation, and we review the current challenges and topics that need to be looked forward to in the future. It was found that the integration of digital twins and AI has significant effects in aerospace flight detection simulation, failure warning, aircraft assembly, and even unmanned flight. In the virtual simulation test of automobile autonomous driving, it can save 80% of the time and cost, and the same road conditions reduce the parameter scale of the actual vehicle dynamics model and greatly improve the test accuracy. In the intelligent manufacturing of production workshops, the establishment of a virtual workplace environment can provide timely fault warning, extend the service life of the equipment, and ensure the overall workshop operational safety. In smart city traffic, the real road environment is simulated, and traffic accidents are restored, so that the traffic situation is clear and efficient, and urban traffic management can be carried out quickly and accurately. Finally, we looked forward to the future of digital twins and AI, hoping to provide a reference for future research in related fields.