Content uploaded by Harco Leslie Hendric Spits Warnars
Author content
All content in this area was uploaded by Harco Leslie Hendric Spits Warnars on Nov 06, 2023
Content may be subject to copyright.
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
A systematic literature review of recent trends and
challenges in digital twin implementation
Ikrar Adinata Arin
Computer Science Department, BINUS
Graduate Program, Doctor of
Computer Science
Bina Nusantara University
Jakarta, Indonesia
ikrar@binus.ac.id
Dina Fitria Murad
Information Systems Department,
BINUS Online Learning
Bina Nusantara University
Jakarta, Indonesia
dmurad@binus.edu
Meyliana
Information System Department, School
of Information Systems
Bina Nusantara University
Jakarta, Indonesia
meyliana@binus.edu
Harco Leslie Hendric Spits Warnars
Computer Science Department, BINUS
Graduate Program, Doctor of
Computer Science
Bina Nusantara University
Jakarta, Indonesia
shendric@binus.edu
Abstract—This paper reviews the latest trends and
challenges in implementing digital twin technology. A digital
twin is a tool used in various industries to improve efficiency,
optimize processes, and enable advanced analysis. The review
involved searching major research databases and search engines
for articles published between 2018 and 2023. The findings
reveal several important trends, including the development of
different types of digital twin dimensions, each with its own
advantages and limitations. The benefits of digital twin
implementation include improved decision-making, increased
productivity, and operational efficiency. However, there are
challenges, such as data integration, security and privacy
concerns, a lack of standardization, and the need for experts to
effectively design and operate digital twins. The implications of
these trends and challenges are discussed regarding their impact
on the successful adoption and implementation of digital twin
technology. The review also highlights the need to address these
challenges and explore new approaches for maximizing the
benefits of digital twin technology. Overall, this comprehensive
review is a valuable resource for researchers, practitioners, and
organizations seeking to understand the current landscape,
identify areas for improvement, and make informed decisions
when implementing digital twin technology.
Keywords—digital twin, trends, challenges, implementation,
systematic literature review
I. INTRODUCTION
With the rapid advancement of information and
communication technology, the industry is transforming
profoundly, replacing analogue, mechanical, and electronic
technology with digital technology. This shift is driven by the
transition to a new information age in business [1]. As
businesses embrace digital transformation, they are propelled
into the fourth industrial revolution, Industry 4.0. This
revolution represents a vision of a society that produces goods
and services with a high degree of automation [2].
In various sectors, new digital technologies, such as digital
twins, the Internet of Things (IoT), virtual reality, and artificial
intelligence, have provided fresh approaches to service
delivery. Among these technologies, the digital twin concept
has gained considerable attention [3].
The idea of the digital twin was first introduced by Grieves
in 2002 as the conceptual paradigm underlying "product
lifecycle management" [4]. It refers to the virtual
representation of an object, system, process, or service that
accurately reflects its real-time state for a specific purpose. By
continuously exchanging and updating descriptive data
through real-time data uploading and large-scale data storage
capabilities, the digital twin remains constantly aware of what
is happening in the physical world. It receives real-time
updates from its physical twin and other nearby digital twins
[5].
Digital twin technology is gaining traction due to its
potential to bridge the gap between the physical and virtual
worlds. The global digital twin market is expected to
experience significant growth in the forecast period, driven by
the increasing adoption of IoT and big data analytics.
Organizations recognize the need to ensure cost-efficient
operations, optimize processes, and reduce time to market,
contributing to the market's expansion [6]. Additionally,
virtual and augmented reality advancements will continue to
shape the creation of digital twins, further fueling market
growth [7].
This paper aims to conduct a systematic literature review
of the latest trends and challenges in implementing digital
twins. It will introduce the digital twin concept, explain its
fundamental principles, and outline the potential benefits of
adopting this technology. Moreover, it will provide an
overview of the research's context, focusing on the latest
trends and challenges in digital twin implementation.
Through this study, we seek to provide valuable insights
for practitioners, researchers, and organizations interested in
adopting digital twins. We hope our findings will serve as a
valuable guide for understanding the latest trends and
overcoming the challenges associated with successful digital
twin implementation.
II. LITERATURE SEARCH STRATEGY
A. Review Method
To achieve the objectives of this study, we will apply the
systematic literature review method developed by Barbara
Kitchenham [8]. This method allows us to perform a thorough
search, a rigorous selection of articles, and a comprehensive
analysis of the relevant literature. We will also use a structured
framework to develop this literature review, focusing on
trends and challenges in digital twin implementation.
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
Fig. 1. Kitchenham systematic literature review flow process
Figure 1. illustrates the various components of the
Systematic Literature Review (SLR). The SLR encompasses
distinct tasks, including the planning, conducting, and
reporting stages. During the planning stage, the requirements
for conducting a systematic review are determined, while the
conducting stage involves describing the literature review
process. To ensure unbiased results, a research protocol is
developed to guide the review's execution, covering research
questions, search methods, study selection criteria, quality
assessment, data extraction, and synthesis procedures.
Throughout the conducting and reporting stages, the research
protocol is employed, assessed, and refined iteratively.
B. Research Question
The following are two relevant research questions for this
paper:
1) What current trends in digital twin implementation
have been identified in the research literature?
2) What are the main challenges faced in implementing
digital twins, both from a technical and non-technical
perspective, based on existing research?
These questions will provide a clear research direction for
identifying the latest trends and challenges in implementing
digital twins. This research has certain limitations and scope.
We will limit this research to literature published between
2018 and 2023 (until the end of April 2023), with publication
language in English. We will also pay attention to relevant
publications, such as research articles, reviews, and
conference proceedings. However, we will avoid publications
that are too general or too specific and are not directly related
to implementing the digital twin.
C. Searching Approach
In the conducting stage, the search procedure involves
several steps: selecting digital libraries, formulating the search
string, conducting a pilot search, refining the search string,
and obtaining a preliminary list of primary studies from the
digital libraries corresponding to the search criteria. Choosing
the appropriate databases before commencing the search is
crucial to enhance the likelihood of discovering highly
relevant content.
To compile the most comprehensive collection of relevant
studies, extensive searches were conducted across the leading
literature databases in the field. A broad perspective was
adopted to ensure a thorough analysis of the literature. The
author explored renowned digital databases, including
Scopus, IEEE Xplore Digital Library, Science Direct, ACM
Digital Library, and Springer Link. To ensure a systematic and
rigorous approach to our research, we developed a
comprehensive review protocol outlined in Table 1. This
review protocol was the foundation for structuring our
methodology and guiding us through the various steps in
conducting this study.
TABLE I. SEARCHING PROTOCOL
Search
Protocol
Description
Type of
publication
Articles published in conferences and academic
journals
Language
English-language publications
Year
2018-2023
Search field
Title, abstracts, and keywords
Search keyword
( "digital twin" AND implementation OR adoption
OR usage AND trend OR development OR
potential AND challenge OR barrier OR issue )
Inclusion
criteria
Publications that explore the discourse surrounding
DT, as well as the implemented applications or
systems
Exclusion
criteria
Restricted to articles solely within the domains of
business, engineering, and computer science
D. Studi Selection
Mendeley - Reference Management Software
(www.mendeley.com) was employed to organize and
maintain the search results. The primary studies were selected
based on the specified inclusion and exclusion criteria. Figure
2. illustrates the comprehensive search process and the
number of studies identified at each stage. Titles and abstracts
were used to exclude certain primary studies, and a further
round of exclusion was conducted based on the full-text
analysis to obtain the final selection of studies.
E. Extract Data
Of the selected studies, 95 papers have been identified for
inclusion in all rounds, which will be thoroughly read and
studied in greater detail.
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
Fig. 2. The selection of primary studies
TABLE II. PRIMARY STUDIES SELECTED
Source
Studies
found
Candidate
studies
Selected
studies
Scopus
204
193
67
IEEE Xplore
38
5
2
ScienceDirect
54
10
4
ACM Digital
147
44
4
Springer Link
347
85
18
Total
790
337
95
Besides the inclusion and exclusion criteria, the
researchers assessed the original studies' quality, relevance to
the research questions, and similarity. The complete text of the
selected articles was thoroughly read, and a quality assessment
checklist was employed to evaluate their quality. Any
duplicate papers by the same authors published in different
sources were removed. After excluding studies based on full-
text selection, 95 studies remained in the final selection.
F. Data Synthesis
Data synthesis involves collecting evidence from multiple
studies to address research questions. Collective evidence
holds more weight than individual pieces, which may have
limited significance. This review incorporates both
quantitative and qualitative data. Different approaches were
used during the reporting phase to combine the gathered data
for different research questions. A narrative synthesis was
predominantly utilized. The data were organized in tables, pie
charts, and bar charts to enhance the presentation of recent
trends and challenges in digital twin implementation found in
the selected studies and ensure data accuracy.
III. RESULT AND FINDING
We employed a meta-analysis methodology to analyze the
findings of selected studies from diverse publications. Many
papers have been published discussing the implementation of
digital twins (DT) across different sectors and industries.
Scopus remains the prominent platform for authors to share
their research, offering a comprehensive database of high-
quality papers. Approximately 70% of the publications were
journal articles; the rest were conference articles.
Fig. 3. Selected Studies
TABLE III. TREND OF PUBLICATIONS
Year
Conference Articles
Journal Articles
2018
-
[9]
2019
[1], [10], [11], [12], [13]
[14], [15], [16]
2020
[17], [18], [19], [20],
[21], [22], [23], [24],
[25], [26], [27]
[28], [29], [30], [31], [32],
[33], [34], [35], [36], [37],
[38], [39], [40], [41], [42],
[43], [7]
2021
[44]
[45], [46], [47], [48], [49],
[50], [6], [3], [4], [51], [52],
[53], [54], [2], [55], [56],
[57], [58]
2022
[59], [60], [61], [62],
[63], [64], [65], [66],
[67], [68], [69]
[70], [71], [72], [73], [74],
[75], [76], [77], [78], [79],
[80], [81], [82], [83], [84],
[85], [86], [87], [88], [89]
2023
-
[90], [91], [92], [93], [94],
[95], [96], [97]
Sub Total
28
67
Total
95
We employed VOSViewer tools to create a text data
mining functionality, enabling us to construct and visualize
co-occurrence networks of key phrases extracted from a
collection of scientific literature, as depicted in Figure 4. The
keywords reflect the theme of studies for a research project in
digital twin areas. The article's keywords help index it for
quick identification.
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
To achieve an image of the keyword that is very readable
during the generation of the keyword co-occurrence network,
the author keywords were used as an alternative to all
keywords input by the software. Additionally, the counting
approach included fractional counting, which led to the
extraction of 2861 keywords from the dataset. A term's
"minimum number of occurrences" for adding it to the
network was set to 4 clusters to create an optimal network. As
a result, 38 items out of 2861 keywords met the requirement.
This condition was reached after multiple experiments to
create an ideal, repeatable, and comprehensible network. The
other network diagrams in this study were made using a
similar experimental strategy.
Fig. 4. Keyword co-occurrence network of DTs from Selected Studies
TABLE IV. MOST ACTIVE KEYWORDS IN THE DT CLUSTER
Cluster 1
Cluster 2
Cluster 3
Cluster 4
company
efficiency
energy
information
machine
problem
process
product
requirement
service
simulation
virtual
representation
adoption
article
barrier
digital
framework
industry
knowledge
lack
number
researcher
review
artificial
interlegence
bim
constuction
field
gap
internet
iot
operation
thing
building
digital twin
technology
dt technology
dts
maintenance
study
The articles in the data set were separated into four clusters
based on a clustering technique. The cluster that a journal was
assigned to using our clustering technique is indicated by a
colour. As can be observed, there is good agreement between
the map's structure and the grouping we produced using our
clustering method. The interpretation of the grouping is also
simple. The four clusters correspond to the study areas listed
above, which have been streamlined for simplicity:
simulation, industrial adoption, technological combination,
and digital twin technology for maintenance.
IV. DISCUSSION
A. DT definitions and concepts
A Digital Twin is initially described as a virtual
representation of a physical product that contains data about
that object [85]. This term has its roots in the field of product
life-cycle management which conceptual idea is represented
by Grieves in 2003 [3]. DT is a virtual representation of real-
world entities and processes synchronized [80] at a specified
frequency and fidelity, as also concluded by the Digital Twin
Consortium [86]. This allows end users to monitor what is
happening inside the physical asset in real time [72]. A digital
twin accurately replicates the real-time performance of
equipment, system, person, or process. DT is a virtual model
[17] replicating the behaviour of an existing or a potential real-
world asset [46], system [98], or multiple systems [22]. It
functions in the present, mimicking the physical thing but with
complete knowledge of its past performance and an exact
assessment of its future potential [70]. A digital twin’s ability
to test scenarios is a vital feature.
Although the concept of digital twins originated in
NASA's Apollo program in 1970 [73], businesses that are
working to realize Industry 4.0 or pursuing future industrial
metaverse [75] projects would greatly benefit from the ability
to visualize [51], simulate [60], and predict [93] using digital
twins of physical assets. Data consumers can utilize a digital
twin to test various scenarios and examine the outcomes and
implications without putting themselves in danger in the real
world [35]. By providing a holistic view of real-time
behaviour in a real-world environment mapped to a constantly
updated virtual model [13], digital twins make it possible to
anticipate maintenance needs [49], optimize performance
[22], and avoid costly failures [93]. The digital twin
technology enables the creation of a virtual replica of a
physical asset, component, product, process, or system/unit. It
utilizes the data obtained from several sensors in the systems
or assets and integrates various technologies such as IoT [11],
[68], [99], IioT [39], [100], AI [4], [60], AR/VR [35], [101],
[102], Machine Learning [13], [41], [103], and big data
analytics [49], [104] for analysis, and makes reasonable
projections about the future process.
B. Difference between digital shadows and digital twins
Current digital 3D models and systems differ significantly
from DT in many ways. A digital shadow (DS) is when a
virtual model replicates the physical model with a one-way
data flow [62]. The diagram in Figure. 5 illustrates the key
difference between a digital shadow and a digital twin. There
is a bi-directional data flow between the digital model and the
tower crane in a digital twin, which is untrue with a digital
shadow.
Fig. 5. Schematic visualization between DS and DT [105]
A digital shadow is a model fed by a one-way data flow
with the state of an existing physical object. A change in the
state of the physical object leads to a change in the digital
object, but not vice versa [95]. In contrast, data flow between
physical and digital objects is fully integrated in both
directions for a digital twin. A digital shadow can thus be seen
as a preliminary stage of a digital twin. The digital shadow
sets the basis for the digital twin, contributing measurement
data or meta-data attributed to a specific object with spatial or
temporal reference. However, it does not yet describe the
physical object and its properties [105].
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
C. RQ1: What current trends in digital twin implementation
have been identified in the research literature?
Adopting digital twin technology has emerged as a
prominent trend in several industrial sectors. This idea entails
producing precise digital representations of real-world
products or systems. The growth of cloud computing and
Internet of Things (IoT) technology supports this trend. The
digital twin makes real-time data collection from several
connected sensors and equipment possible, which can
subsequently be utilized to monitor and manage physical
systems. Better modelling and forecasting are made possible
by improved data integration between diverse systems and
entities, boosting operational effectiveness, enhancing
decision-making, and fostering product and service
innovation. Several digital twin platforms have been created,
especially for certain sectors, including industry, energy, and
transportation. This pattern demonstrates the growing interest
and investment in digital twin technologies among businesses
looking to enhance their operations.
The use of digital twins in numerous application sectors is
another clearly discernible trend. Initially, the manufacturing
sector employed the digital twin primarily to monitor and
improve the operation of machinery and production methods.
However, over time, applications for digital twins have spread
to other industries, including smart cities, energy,
transportation, and health. Digital twins, for instance, can be
used to optimize energy output and track grid performance in
the energy industry. Digital twins can be utilized for patient
care planning and medical simulations in the healthcare
industry. This development demonstrates that the digital
twin's potential to boost productivity and creativity is not
restricted to a single sector. Using the digital twin also
promotes improved data integration and collaboration among
diverse systems and organizations. As part of the literature
research, the authors' results found that there were six
prominent digital twin activity groups. We call this the digital
twin application, which is written in the table below:
TABLE V. DIGITAL TWIN APPLICATION
No
Digital twin
application
Description
1
Twins for system
prediction
A digital twin designed to forecast
intricate systems
2
Twins for system
simulation
A digital twin developed to simulate
the behavior of intricate systems
3
Twins for asset
interoperability
A digital twin geared toward common
data formats and streamlined data
extraction in complex systems
4
Twins for maintenance
A digital twin geared toward assisting
with maintenance-related use cases
5
Twins for system
visualization
A digital twin geared toward
visualizing a complex system (e.g., in
3D)
6
Twins for product
simulation
A digital twin geared toward
simulating the behavior of (future)
products (mostly during the design
phase)
Among the analyzed digital twin projects, 30% are
categorized as predictive digital twins, which aim to forecast
the behaviour of complete systems (such as factories,
buildings, wind farms, or cities) throughout their "operate" or
"optimize" life cycle stages. These digital twins concentrate
on predicting physical systems' future state and behaviour,
utilizing current data and pertinent operational history. At the
heart of predictive digital twins are predictive models
employed to anticipate future outcomes. The concept of
digital twins, as depicted in Figure. 6, can be described using
three primary dimensions. Each axis of the cuboid represents
one dimension of the digital twin.
Fig. 6. Three dimession of DT‘s
The concept of digital twins can be understood through
three key dimensions. An axis of the cuboid represents each
dimension of the digital twin.
• Life cycle phase: The X-axis illustrates a digital twin's
six life cycle phases, from design to decommissioning.
• Hierarchical levels: The Y-axis signifies the five
hierarchical levels of a digital twin, spanning from
information to multi-system.
• Use/purpose of implementation: The Z-axis portrays
the seven prevalent applications of digital twins,
including simulation and prediction.
Practitioners and academics must watch advancements
and breakthroughs in using digital twin technology to
comprehend and follow these trends. By following trends,
enterprises can utilize the full potential of this technology to
enhance operations and gain a competitive advantage.
Recognizing these patterns can also promote stakeholder
cooperation and lead to revisions to pertinent standards and
laws. There has been a major trend in applying digital twin
technologies in recent years.
The development of digital twin platforms, acceptance in
various application domains, improved collaboration and data
integration, and a greater emphasis on data security and
privacy. The digital twin ecosystem comprises
software/solution providers, system providers, associated
technology providers, and distributors.
Additionally, it represents the digital twin providers
offering a comprehensive range of products/solutions utilized
by the aerospace, automotive & transportation, energy &
utilities, oil & gas, infrastructure, healthcare, agriculture,
retail, telecommunications, and other industries.
D. RQ2: What are the main challenges faced in
implementing digital twins, both from a technical and
non-technical perspective, based on existing research?
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
E. RQ2: What are the main challenges faced in
implementing digital twins, both from a technical and
non-technical perspective, based on existing research?
The potential for using digital twin technology is
incredibly exciting, but numerous obstacles must be solved.
Some major obstacles to using digital twin technology explain
why businesses and researchers are very concerned about it.
Complex data integration is one of the main obstacles to
implementing digital twins. To create a digital twin, data must
be gathered from several sources, such as sensors, the Internet
of Things (IoT), geographic information systems, etc.
However, the format of this data is frequently uneven and
frequently dispersed across several systems and platforms.
This difficult data integration may hamper the development of
precise, real-time digital models. Organizational and cultural
variables play a role in the difficulties associated with
integrating digital twins. Therefore, a successful data
integration plan must guarantee proper data availability and
quality. The privacy and security of data are still other issues.
Sensitive data, including details on how physical systems
TABLE VI. THE FIVE HIERARCHICAL LEVELS OF A DIGITAL TWIN
Five hierarchical levels
Description
Example Ref.
Multi-system
twinning
Digital representations of multiple interconnected
systems operating as a cohesive entity, enabling
unparalleled visibility, testing, and monitoring of
critical business metrics through data-driven
approaches.
Virtual representation of multiple interconnected
systems operating collaboratively, including those
utilized in industrial manufacturing [9], [16], [22], [30],
[38], [44], [50], [56], [70], [76], [77], [102], smart city
infra [31], [61], [92], supply chain [24], [80], [90],
healthcare[13], [66], [81], [86], [87], [91], and service
[7], [11], [49], [58], [63], [66], [76], [77].
System
twinning
Digital representations that encompass the
integration of multiple products, processes, and
workflows, surpassing the limitations of merely
replicating individual objects.
Virtual representation of a vaccine production process
or entire factory with multiple production lines [17],
[21], [45], [54], [65], [100].
Product
twinning
Digital representations that depict the collaborative
functioning of components or parts, showcasing
their interoperability at a product level.
Virtual representation of a rotating robotic arm [15],
[19], [26], [53], [69], [77], [101].
Component
twinning
Digital representations of discrete components or
constituent parts within a physical object.
Virtual representation of a bolt or a bearing in a robotic
arm[15], [53].
Information
twinning
Digital depictions of data or information.
An operation manual in digital format.[28], [73], [89],
[100], [105].
Huerarchi
cal levels
TABLE VII. THE SIX LIVECYCLE PHASES IN WHICH DIGITAL TWIN ARE APPLIED
Six lifecycle phases
Description
Example
Decommission
The decommissioning phase encompasses the
process of retiring or sunsetting a digital twin,
which involves removing it from active use.
Leveraging digital twins to remotely decommission
inactive devices and subsequently retire the associated
digital twin.
Optimize
In the optimize phase, existing capability
information and statistical tolerancing techniques
are employed to enhance the development of
detailed design elements, predict performance, and
optimize operations.
Using digital twins for comprehensive testing,
generating valuable insights to anticipate future
performance and detect potential failures.
Maintain
The maintenance phase encompasses the
implementation of changes to hardware, software,
and documentation, aiming to uphold operational
effectiveness. This phase involves making
modifications to resolve issues, improve security,
or address user requirements.
Leveraging digital twins to perform routine maintenance
tasks, including deploying Over-the-Air (OTA) updates
for system configuration or cybersecurity purposes.
Operate
During the operation phase, online digital twins are
utilized in active deployments by end-users. In this
phase, common operational tasks consist of
extracting sensor data and remotely orchestrating
devices.
Utilizing digital twins to retrieve real-time sensor data
from a rotating robotic arm within a production line or
perform over-the-air updates to device configurations.
Build
The build phase involves leveraging the previously
defined code requirements to construct the tangible
software-based digital twin. This phase
encompasses tasks such as data management,
configuration provisioning, repository
management, and reporting.
Leveraging digital twins to virtually construct and
simulate prototypes, eliminating the need for costly
physical counterparts during the testing phase.
Design
During the design phase, requirements are
collected, and one or more designs are developed
to meet the desired outcome. These designs may
pertain to components, products, processes, or
systems, aiming to achieve the intended result.
Utilizing digital twins as the primary source of data,
including object properties and parameter values, upon
which virtual representations can be constructed.
Lifecycle phases
TABLE VIII. THE SEVEN MOST COMMON USES OF DIGITAL TWINS
DT uses
1. Digitise
2. Visualize
3. Simulate
4. Emulate
5. Extract
6. Orchestrate
7. Predict
Digital twin
description
Digitized
information of
any kind
Fundamental
digital
representation
of a physical
object
Simulation
model of a
physical
system in its
environment
Emulation
model
mirroring a
physical
system using
real software
Extraction
model of real-
time data
streams:
physical- to-
virtual system
Orchestration
model facilitating
virtual control
and updates of
physical devices
Prediction
model
forecasting
future behavior
of the physical
system
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
function and client information, is gathered and processed to
create a digital twin. Threats include potential data leaks and
vulnerability to cyberattacks. To protect digital twin data,
enterprises must create stringent security policies, practice
good cybersecurity, and use suitable encryption and
authentication technology. The greater emphasis on data
security and privacy is an important trend in adopting digital
twins. As adoption spreads, digital twin system susceptibility
to hacker attacks and data theft is problematic. To safeguard
the data gathered and processed in the context of the digital
twin, it is crucial to provide strong security solutions. The
necessity for more effort and awareness in securing the digital
twin infrastructure is reflected in this development. Additional
difficulties include the intricacy and scalability of digital twin
technology. For effective modelling and simulation, a digital
twin must be able to handle enormous amounts of data and
process it quickly. This intricacy may impact the effectiveness
and performance of digital twin systems. To retain optimal
performance, businesses must consider scalable technology
infrastructures, employ cutting-edge algorithms and
modelling methodologies, and carry out optimizations. The
absence of uniform standards, cost, and interoperability is yet
another difficulty in deploying digital twins. To ensure
compatibility and seamless system integration, given the
range of components and platforms that make up digital twins,
it is crucial to have clear standards and robust interoperability.
The implementation of digital twin technologies on a large
scale and data sharing and collaboration between businesses
may be hampered by a lack of standards. A shift in workplace
culture is frequently necessary when introducing new
technologies. Some of these issues are overcoming opposition
to change, raising public knowledge of the advantages of
digital twins, and enhancing organizational capacity to
manage and use this technology properly. In conclusion,
several obstacles must be resolved to adopt digital twin
technology successfully.
TABLE IX. CHALLENGE IN DT IMPLEMENTATION
DT’s challenges
Reference
data integration
[2], [26], [31], [33], [42], [63],
[67], [88]
privacy and security
[35], [60], [80], [91]
scalability & standarization
[9], [48], [77], [84]
effectiveness and performance
[25], [47], [72], [77]
data sharing
[9], [13], [72], [87]
cost
[84], [85], [97]
lack of technology knowledge
[56], [60]
V. FUTURE RESEARCH DIRECTION
As with any uncertain technology initiative, the adopters
of digital twin technology should also consider some of the
future research in using this technology. Digital twin
technology is attracting attention in various industries, and
field service management (FSM) is expected to be a field of
application. FSM involves managing and providing field
services such as maintenance, repair, and installation of
complex equipment and systems. Traditionally, FSM has
relied on manual planning, verbal communication, and
documentation. However, the advent of digital twin
technology has created an opportunity to change how FSMs
operate. One potential application of digital twin technology
in FSM is real-time monitoring and predictive maintenance.
By using digital twins of existing devices or systems in the
field, organizations can monitor performance in real time and
get accurate information about device health and performance.
This allows you to proactively monitor potential damage and
failures so that maintenance can be performed before
problems become more serious. This way, businesses can
reduce downtime, increase efficiency, and optimize resource
utilization. In addition, digital twins also enable accurate
simulation and modelling. Digital twin technology improves
collaboration between field technicians, support teams, and
control centres. Through the digital twin, field technicians can
access accurate information about the equipment they are
working on, including repair manuals, documents, and
maintenance history. This enhanced collaboration allows field
technicians to work more efficiently, reducing problem-
resolution time and increasing customer satisfaction. In
summary, digital twin technology has great potential to
improve FSM efficiency, productivity, and customer
satisfaction. This technology provides real-time monitoring,
predictive maintenance, accurate simulation and modelling,
and improved collaboration.
VI. CONCLUSION
This systematic literature review provides insights into the
recent trends and challenges in digital twin implementation.
The findings highlight the development of different digital
twin platforms and their benefits, including improved
decision-making, increased productivity, and operational
efficiency. However, challenges such as data integration,
security and privacy concerns, lack of standardization, and the
need for expertise in designing and operating digital twins are
identified. The review emphasizes the need to address these
challenges and explore new approaches to maximizing the
benefits of digital twin technology. It is a valuable resource
for researchers, practitioners, and organizations seeking to
understand the current landscape, identify areas for
improvement, and make informed decisions when
implementing digital twin technology. As digital twin
technology continues to evolve, staying updated with the latest
research and advancements is essential. Future research can
address specific challenges, such as standardization efforts,
data integration techniques, security frameworks, and
effective training programs to develop expertise in digital twin
implementation. By overcoming these challenges, digital twin
technology can unlock its full potential and revolutionize
various industries.
REFERENCES
[1] K. Schützer, J. de Andrade Bertazzi, C. Sallati, R. Anderl, and E.
Zancul, “Contribution to the development of a Digital Twin based on
product lifecycle to support the manufacturing process,” P. G.D., Ed.,
Lab. for Computer Integrated Design and Manufacturing, Methodist
University of Piracicaba, Rod. Luis Ometto, km 24, Santa Bárbara
d‘Oeste, Brazil: Elsevier B.V., 2019, pp. 82–87. doi:
10.1016/j.procir.2019.03.212.
[2] S. Y. Teng, M. Touš, W. D. Leong, B. S. How, H. L. Lam, and V.
Máša, “Recent advances on industrial data-driven energy savings:
Digital twins and infrastructures,” Renewable and Sustainable Energy
Reviews, vol. 135, p. 110208, 2021, doi:
https://doi.org/10.1016/j.rser.2020.110208.
[3] G. B. Ozturk, “Digital Twin Research in the AECO-FM Industry,”
Journal of Building Engineering, vol. 40, p. 102730, 2021, doi:
https://doi.org/10.1016/j.jobe.2021.102730.
[4] A. To et al., “Drone-Based AI and 3D Reconstruction for Digital Twin
Augmentation,” Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics), vol. 12774 LNCS, pp. 511–529, 2021, doi:
10.1007/978-3-030-77626-8_35.
[5] B. R. Barricelli, E. Casiraghi, and D. Fogli, “A survey on digital twin:
Definitions, characteristics, applications, and design implications,”
IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2953499.
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
[6] D. Petrova-Antonova and S. Ilieva, “Digital twin modeling of smart
cities,” Advances in Intelligent Systems and Computing, vol. 1253
AISC, pp. 384–390, 2021, doi: 10.1007/978-3-030-55307-4_58.
[7] O. Stoll, S. West, P. Gaiardelli, D. Harrison, and F. J. Corcoran, “The
Successful Commercialization of a Digital Twin in an Industrial
Product Service System,” IFIP Adv Inf Commun Technol, vol. 592
IFIP, pp. 275–282, 2020, doi: 10.1007/978-3-030-57997-5_32.
[8] B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey,
and S. Linkman, “Systematic literature reviews in software
engineering – A systematic literature review,” Inf Softw Technol, vol.
51, no. 1, pp. 7–15, Jan. 2009, doi: 10.1016/j.infsof.2008.09.009.
[9] S. Singh, E. Shehab, N. Higgins, K. Fowler, T. Tomiyama, and C.
Fowler, “Challenges of Digital Twin in High Value Manufacturing,”
vol. 2018-Novem, 2018, doi: 10.4271/2018-01-1928.
[10] D. Burnett, J. Thorp, D. Richards, K. Gorkovenko, and D. Murray-
Rust, “Digital Twins as a Resource for Design Research,” in
Proceedings of the 8th ACM International Symposium on Pervasive
Displays, in PerDis ’19. New York, NY, USA: Association for
Computing Machinery, 2019. doi: 10.1145/3321335.3329685.
[11] I. Donoghue, L. Hannola, and A. Mikkola, “The value of digital twins
and IoT based services in creating lifecycle value in B2B
manufacturing companies,” K. D.F., A. T.R., K. D.C., N. K., and S.
H.-J., Eds., Industrial Engineering and Management, Lappeenranta-
Lahti University of Technology (LUT), Finland: Institute of Electrical
and Electronics Engineers Inc., 2019. doi:
10.23919/PICMET.2019.8893904.
[12] T. Bauer, P. Oliveira Antonino, and T. Kuhn, “Towards Architecting
Digital Twin-Pervaded Systems,” Fraunhofer Institute for
Experimental Software Engineering, Kaiserslautem, Germany:
Institute of Electrical and Electronics Engineers Inc., 2019, pp. 66–69.
doi: 10.1109/SESoS/WDES.2019.00018.
[13] C. M. Ezhilarasu, Z. Skaf, and I. K. Jennions, “Understanding the role
of a digital twin in integrated vehicle health management (IVHM),”
IVHM Centre, Cranfield UniversityMK430AL, United Kingdom:
Institute of Electrical and Electronics Engineers Inc., 2019, pp. 1484–
1491. doi: 10.1109/SMC.2019.8914244.
[14] K. Kannan and N. Arunachalam, “A Digital Twin for Grinding Wheel:
An Information Sharing Platform for Sustainable Grinding Process,”
Journal of Manufacturing Science and Engineering, Transactions of
the ASME, vol. 141, no. 2, 2019, doi: 10.1115/1.4042076.
[15] T. Hoebert, W. Lepuschitz, E. List, and M. Merdan, “Cloud-Based
Digital Twin for Industrial Robotics,” Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics), vol. 11710 LNAI, pp. 105–116,
2019, doi: 10.1007/978-3-030-27878-6_9.
[16] Z. Zhu, C. Liu, and X. Xu, “Visualisation of the Digital Twin data in
manufacturing by using Augmented Reality,” Procedia CIRP, vol. 81,
pp. 898–903, 2019, doi: https://doi.org/10.1016/j.procir.2019.03.223.
[17] Y. Jeong, E. Flores-Garcia, and M. Wiktorsson, “A Design of Digital
Twins for Supporting Decision-Making in Production Logistics,” in
Proceedings - Winter Simulation Conference, in WSC ’20, vol. 2020-
Decem. IEEE Press, 2020, pp. 2683–2694. doi:
10.1109/WSC48552.2020.9383863.
[18] A. Ebrahimi et al., “A digital twin approach to predict and compensate
distortion in a High Pressure Die Casting (HPDC) process chain,”
Fraunhofer Institute for Manufacturing Technology and Advanced
Materials IFAM, Wiener Straße 12, Bremen, 28359, Germany:
Elsevier B.V., 2020, pp. 144–149. doi: 10.1016/j.promfg.2020.11.026.
[19] È. Pairet, P. Ardón, X. Liu, J. Lopes, H. Hastie, and K. S. Lohan, “A
Digital Twin for Human-Robot Interaction,” in Proceedings of the
14th ACM/IEEE International Conference on Human-Robot
Interaction, in HRI ’19. IEEE Press, 2020, p. 372.
[20] S. A. Deryabin, I. O. Temkin, and S. V Zykov, “About some issues of
developing Digital Twins for the intelligent process control in
quarries,” C. M., T. C., Z.-M. C., H. R.J., J. L.C., and J. L.C., Eds.,
National University of Science and Technology MISiS, 4 Leninsky
Av., Mocsow, 119049, Russian Federation: Elsevier B.V., 2020, pp.
3210–3216. doi: 10.1016/j.procs.2020.09.128.
[21] G. Schuh, C. Kelzenberg, J. Wiese, and N. Kessler, “Creation of
digital production twins for the optimization of value creation in single
and small batch production,” G. R.X. and E. K., Eds., Laboratory for
Machine Tools and Produtcion Engineering (WZL), RWTH Aachen
University, Campus-Boulevard 3052074, Germany: Elsevier B.V.,
2020, pp. 222–227. doi: 10.1016/j.procir.2020.04.125.
[22] G. Shao, S. Jain, C. Laroque, L. H. Lee, P. Lendermann, and O. Rose,
“Digital Twin for Smart Manufacturing: The Simulation Aspect,” in
Proceedings of the Winter Simulation Conference, in WSC ’19. IEEE
Press, 2020, pp. 2085–2098.
[23] M. Dietz, M. Vielberth, and G. Pernul, “Integrating Digital Twin
Security Simulations in the Security Operations Center,” in
Proceedings of the 15th International Conference on Availability,
Reliability and Security, in ARES ’20. New York, NY, USA:
Association for Computing Machinery, 2020. doi:
10.1145/3407023.3407039.
[24] K. Wang et al., “Simulation-Based Digital Twin Development for
Blockchain Enabled End-to-End Industrial Hemp Supply Chain Risk
Management,” in Proceedings - Winter Simulation Conference, in
WSC ’20, vol. 2020- Decem. IEEE Press, 2020, pp. 3200–3211. doi:
10.1109/WSC48552.2020.9384115.
[25] D. Gyulai, J. Bergmann, A. Lengyel, B. Kadar, and D. Czirko,
“Simulation-Based Digital Twin of a Complex Shop-Floor Logistics
System,” in Proceedings - Winter Simulation Conference, in WSC ’20,
vol. 2020- Decem. IEEE Press, 2020, pp. 1849–1860. doi:
10.1109/WSC48552.2020.9383936.
[26] A. Martins, H. Costelha, and C. Neves, “Supporting the Design,
Commissioning and Supervision of Smart Factory Components
through their Digital Twin,” in 2020 IEEE International Conference
on Autonomous Robot Systems and Competitions (ICARSC), 2020, pp.
114–119. doi: 10.1109/ICARSC49921.2020.9096072.
[27] R. Gaha, A. Durupt, and B. Eynard, “Towards the implementation of
the Digital Twin in CMM inspection process: Opportunities,
challenges and proposals,” Université de Technologie de Compiègne,
Laboratoire Roberval, rue du Dr Schweitzer, CS 60319, Compiègne
cedex, 60203, France: Elsevier B.V., 2020, pp. 216–221. doi:
10.1016/j.promfg.2021.07.033.
[28] F. Noël, G. Alpan, and F. Mangione, “A Complete Digital Chain to
Enable the Digital Twin of a Shop Floor,” IFIP Adv Inf Commun
Technol, vol. 594, pp. 128–138, 2020, doi: 10.1007/978-3-030-62807-
9_11.
[29] D. Petrova-Antonova, I. Spasov, I. Krasteva, I. Manova, and S. Ilieva,
“A Digital Twin Platform for Diagnostics and Rehabilitation of
Multiple Sclerosis,” Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics), vol. 12249 LNCS, pp. 503–518, 2020, doi:
10.1007/978-3-030-58799-4_37.
[30] N. Taylor, C. Human, K. Kruger, A. Bekker, and A. Basson,
“Comparison of digital twin development in manufacturing and
maritime domains,” Studies in Computational Intelligence, vol. 853,
pp. 158–170, 2020, doi: 10.1007/978-3-030-27477-1_12.
[31] Q. Lu et al., “Developing a Digital Twin at Building and City Levels:
Case Study of West Cambridge Campus,” Journal of Management in
Engineering, vol. 36, no. 3, 2020, doi: 10.1061/(ASCE)ME.1943-
5479.0000763.
[32] I. Barosan, A. A. Basmenj, S. G. R. Chouhan, and D. Manrique,
“Development of a virtual simulation environment and a digital twin
of an autonomous driving truck for a distribution center,”
Communications in Computer and Information Science, vol. 1269
CCIS, pp. 542–557, 2020, doi: 10.1007/978-3-030-59155-7_39.
[33] T. R. Wanasinghe et al., “Digital Twin for the Oil and Gas Industry:
Overview, Research Trends, Opportunities, and Challenges,” IEEE
Access, vol. 8, pp. 104175–104197, 2020, doi:
10.1109/ACCESS.2020.2998723.
[34] K. Wärmefjord, R. Söderberg, B. Schleich, and H. Wang, “Digital
twin for variation management: A general framework and
identification of industrial challenges related to the implementation,”
Applied Sciences (Switzerland), vol. 10, no. 10, 2020, doi:
10.3390/APP10103342.
[35] M. Bevilacqua et al., “Digital Twin Reference Model Development to
Prevent Operators’ Risk in Process Plants,” Sustainability, vol. 12, no.
3. 2020. doi: 10.3390/su12031088.
[36] R. A. Iureva, A. S. Kremlev, V. Subbotin, D. V. Kolesnikova, and Y.
S. Andreev, “Digital twin technology for pipeline inspection,” Smart
Innovation, Systems and Technologies, vol. 193, pp. 329–339, 2020,
doi: 10.1007/978-981-15-5925-9_28.
[37] V. Temkina, A. Medvedev, and A. Mayzel, “Digital twin-based
research on fiber optic current sensor behavior and stability,” Lecture
Notes in Computer Science (including subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics), vol.
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
12526 LNCS, pp. 373–383, 2020, doi: 10.1007/978-3-030-65729-
1_33.
[38] R. B. Roy et al., “Digital twin: current scenario and a case study on a
manufacturing process,” International Journal of Advanced
Manufacturing Technology, vol. 107, no. 9–10, pp. 3691–3714, Apr.
2020, doi: 10.1007/S00170-020-05306-W.
[39] M. Ghita, B. Siham, M. Hicham, A. Abdelhafid, and D. Laurent,
“Digital twins: development and implementation challenges within
Moroccan context,” SN Appl Sci, vol. 2, no. 5, p. 885, Mar. 2020, doi:
10.1007/s42452-020-2691-6.
[40] Q. Lu, X. Xie, J. Heaton, A. K. Parlikad, and J. Schooling, “From BIM
towards digital twin: Strategy and future development for smart asset
management,” Studies in Computational Intelligence, vol. 853, pp.
392–404, 2020, doi: 10.1007/978-3-030-27477-1_30.
[41] M. S. Ibrahim et al., “Machine Learning and Digital Twin Driven
Diagnostics and Prognostics of Light-Emitting Diodes,” Laser Photon
Rev, vol. 14, no. 12, 2020, doi: 10.1002/lpor.202000254.
[42] J. Bickford, D. L. Van Bossuyt, P. Beery, and A. Pollman,
“Operationalizing digital twins through model-based systems
engineering methods,” Systems Engineering, vol. 23, no. 6, pp. 724–
750, 2020, doi: 10.1002/sys.21559.
[43] A. N. Shabelnikov and I. A. Olgeyzer, “Technology and Mathematical
Basis of Digital Twin Creation in Railway Infrastructure,” Advances
in Intelligent Systems and Computing, vol. 1156 AISC, pp. 688–695,
2020, doi: 10.1007/978-3-030-50097-9_70.
[44] H. Latif, G. Shao, and B. Starly, “A Case Study of Digital Twin for a
Manufacturing Process Involving Human Interactions,” in
Proceedings of the Winter Simulation Conference, in WSC ’20. IEEE
Press, 2021, pp. 2659–2670.
[45] A. Koulouris, N. Misailidis, and D. Petrides, “Applications of process
and digital twin models for production simulation and scheduling in
the manufacturing of food ingredients and products,” Food and
Bioproducts Processing, vol. 126, pp. 317–333, 2021, doi:
https://doi.org/10.1016/j.fbp.2021.01.016.
[46] Í. A. Fonseca and H. M. Gaspar, “Challenges when creating a cohesive
digital twin ship: a data modelling perspective,” Ship Technology
Research, vol. 68, no. 2, pp. 70–83, 2021, doi:
10.1080/09377255.2020.1815140.
[47] J. Liu, J. Liu, C. Zhuang, Z. Liu, and T. Miao, “Construction method
of shop-floor digital twin based on MBSE,” J Manuf Syst, vol. 60, pp.
93–118, 2021, doi: 10.1016/j.jmsy.2021.05.004.
[48] Y.-X. Zhang et al., “Digital twin accelerating development of
metallized film capacitor: Key issues, framework design and
prospects,” Energy Reports, vol. 7, pp. 7704–7715, 2021, doi:
10.1016/j.egyr.2021.10.116.
[49] J. Meierhofer, L. Schweiger, and L. Schreuder, “Digital Twin Based
Decision Support Services in Business Operations,” pp. 117–129,
2021, doi: 10.1007/978-3-030-72090-2_11.
[50] E. Hajjem, H. H. Benderbal, N. Hamani, and A. Dolgui, “Digital Twin
Framework for Reconfigurable Manufacturing Systems: Challenges
and Requirements,” IFIP Adv Inf Commun Technol, vol. 631 IFIP, pp.
553–562, 2021, doi: 10.1007/978-3-030-85902-2_59.
[51] S. M. E. Sepasgozar, F. K. P. Hui, S. Shirowzhan, M. Foroozanfar, L.
Yang, and L. Aye, “Lean practices using building information
modeling (Bim) and digital twinning for sustainable construction,”
Sustainability (Switzerland), vol. 13, no. 1, pp. 1–22, 2021, doi:
10.3390/su13010161.
[52] N. Gou, K. Cheng, and D. Huo, “Multiscale modelling and analysis
for design and development of a high-precision aerostatic bearing
slideway and its digital twin,” Machines, vol. 9, no. 5, 2021, doi:
10.3390/machines9050085.
[53] R. Michalík, J. Hrbček, and A. Janota, “Parallel robot controlled by
plc and its digital twin,” TransNav, vol. 15, no. 4, pp. 867–871, 2021,
doi: 10.12716/1001.15.04.19.
[54] R. Ward, P. Soulatiantork, S. Finneran, R. Hughes, and A. Tiwari,
“Real-time vision-based multiple object tracking of a production
process: Industrial digital twin case study,” Proc Inst Mech Eng B J
Eng Manuf, vol. 235, no. 11, pp. 1861–1872, 2021, doi:
10.1177/09544054211002464.
[55] D. T. Kutzke, J. B. Carter, and B. T. Hartman, “Subsystem selection
for digital twin development: A case study on an unmanned
underwater vehicle,” Ocean Engineering, vol. 223, 2021, doi:
10.1016/j.oceaneng.2021.108629.
[56] C. L. Gargalo et al., “Towards the Development of Digital Twins for
the Bio-manufacturing Industry,” Adv Biochem Eng Biotechnol, vol.
176, pp. 1–34, 2021, doi: 10.1007/10_2020_142.
[57] G. Bhatti, H. Mohan, and R. Raja Singh, “Towards the future of ssmart
electric vehicles: Digital twin technology,” Renewable and
Sustainable Energy Reviews, vol. 141, 2021, doi:
10.1016/j.rser.2021.110801.
[58] A. E. Onile, R. Machlev, E. Petlenkov, Y. Levron, and J. Belikov,
“Uses of the digital twins concept for energy services, intelligent
recommendation systems, and demand side management: A review,”
Energy Reports, vol. 7, pp. 997–1015, 2021, doi:
10.1016/j.egyr.2021.01.090.
[59] K. Eriksson, A. Alsaleh, S. Behzad Far, and D. Stjern, “Applying
Digital Twin Technology in Higher Education: An Automation Line
Case Study,” N. A.H.C., S. A., H. D., and H. M., Eds., University
West, Department of Engineering Science, Trollhättan, SE-461 86,
Sweden: IOS Press BV, 2022, pp. 461–472. doi:
10.3233/ATDE220165.
[60] W. Strielkowski, G. Rausser, and E. Kuzmin, “Digital Revolution in
the Energy Sector: Effects of Using Digital Twin Technology,” in
Lecture Notes in Information Systems and Organisation, Springer
Science and Business Media Deutschland GmbH, 2022, pp. 43–55.
doi: 10.1007/978-3-030-94617-3_4.
[61] A. Dulaimi, R. Hamida, M. Naser, and M. Mawed, “DIGITAL TWIN
SOLUTION IMPLEMENTED ON ENERGY HUB TO FOSTER
SUSTAINABLE SMART ENERGY CITY, CASE STUDY OF
SUSTAINABLE SMART ENERGY HUB,” W. C.C. and S. S., Eds.,
Enova by Veolia, Dubai, United Arab Emirates: Copernicus
Publications, 2022, pp. 41–48. doi: 10.5194/isprs-annals-X-4-W3-
2022-41-2022.
[62] J. Wong, P. Hoong, E. Teo, and A. Lin, “Digital Twin: A
Conceptualization of the Task-Technology Fit for Individual Users in
the Building Maintenance Sector,” B. M., A.-M. G., W. R., and M. E.,
Eds., Department of the Built Environment, College of Design and
Engineering, National University of Singapore, Singapore, Singapore:
Institute of Physics, 2022. doi: 10.1088/1755-1315/1101/9/092041.
[63] F. N. Laukotka, C. Rennpferdt, and D. Krause, “Digital Twins and
Product-Service Systems: A Synergy with Challenges and
Opportunities,” Hamburg University of Technology, Germany:
Cambridge University Press, 2022, pp. 1639–1648. doi:
10.1017/pds.2022.166.
[64] G. P. C. Tancredi, G. Vignali, and E. Bottani, “Implementation of a
remote control and system monitoring via a Digital Twin on an
industrial pilot plant,” B. A., D. A., B. H.H., I. D., L. D., and S. F.,
Eds., Department of Engineering and Architecture, University of
Parma, Parco Area delle Scienze 181/A, Parma, 43124, Italy: Elsevier
B.V., 2022, pp. 1128–1133. doi: 10.1016/j.ifacol.2022.09.541.
[65] C. Franciosi, S. Miranda, C. R. Veneroso, and S. Riemma, “Improving
industrial sustainability by the use of digital twin models in
maintenance and production activities,” B. B., R. D., E. C., P. A., and
S. S., Eds., Université de Lorraine, CNRS, CRAN, Nancy, F-54000,
France: Elsevier B.V., 2022, pp. 37–42. doi:
10.1016/j.ifacol.2022.09.215.
[66] S. Maleki, N. Jazdi, and B. Ashtari, “Intelligent Digital Twin in Health
Sector: Realization of a Software-Service for Requirements- and
Model-based-Systems-Engineering,” B. B., R. D., E. C., P. A., and S.
S., Eds., Institute of Industrial Automation and Software Engineering,
Stuttgart, 70550, Germany: Elsevier B.V., 2022, pp. 79–84. doi:
10.1016/j.ifacol.2022.09.187.
[67] F. A. Abumadi, C. Semeraro, A. G. Olabi, and M. Dassisti, “Key
Research Challenges in Digital Twin Applications for
Demanufacturing,” B. A., D. A., B. H.H., I. D., L. D., and S. F., Eds.,
Department of Industrial and Management Engineering, University of
Sharjah, Sharjah, United Arab Emirates: Elsevier B.V., 2022, pp.
2551–2556. doi: 10.1016/j.ifacol.2022.10.093.
[68] P. Empl, D. Schlette, D. Zupfer, and G. Pernul, “SOAR4IoT: Securing
IoT Assets with Digital Twins,” in Proceedings of the 17th
International Conference on Availability, Reliability and Security, in
ARES ’22. New York, NY, USA: Association for Computing
Machinery, 2022. doi: 10.1145/3538969.3538975.
[69] G. Walravens, H. M. Muctadir, and L. Cleophas, “Virtual Soccer
Champions: A Case Study on Artifact Reuse in Soccer Robot Digital
Twin Construction,” in Proceedings of the 25th International
Conference on Model Driven Engineering Languages and Systems:
Companion Proceedings, in MODELS ’22. New York, NY, USA:
979-8-3503-3954-3/23/$31.00 ©2023 IEEE
Association for Computing Machinery, 2022, pp. 463–467. doi:
10.1145/3550356.3561586.
[70] M. Pantelidakis, K. Mykoniatis, J. Liu, and G. Harris, “A digital twin
ecosystem for additive manufacturing using a real-time development
platform,” International Journal of Advanced Manufacturing
Technology, vol. 120, no. 9–10, pp. 6547–6563, Jun. 2022, doi:
10.1007/S00170-022-09164-6/FULLTEXT.HTML.
[71] Z. Jiang, H. Lv, Y. Li, and Y. Guo, “A novel application architecture
of digital twin in smart grid,” J Ambient Intell Humaniz Comput, vol.
13, no. 8, pp. 3819–3835, Aug. 2022, doi: 10.1007/S12652-021-
03329-Z.
[72] H. H. Hosamo, H. K. Nielsen, A. N. Alnmr, P. R. Svennevig, and K.
Svidt, “A review of the Digital Twin technology for fault detection in
buildings,” Front Built Environ, vol. 8, 2022, doi:
10.3389/fbuil.2022.1013196.
[73] E. Ferko, A. Bucaioni, and M. Behnam, “Architecting Digital Twins,”
IEEE Access, vol. 10, pp. 50335–50350, 2022, doi:
10.1109/ACCESS.2022.3172964.
[74] K. Rogage, E. Mahamedi, I. Brilakis, and M. Kassem, “Beyond digital
shadows: Digital Twin used for monitoring earthwork operation in
large infrastructure projects,” AI in Civil Engineering, vol. 1, no. 1,
Dec. 2022, doi: 10.1007/S43503-022-00009-5.
[75] Z. Lv, L. Qiao, Y. Li, Y. Yuan, and F.-Y. Wang, “BlockNet: Beyond
reliable spatial Digital Twins to Parallel Metaverse,” Patterns, vol. 3,
no. 5, 2022, doi: 10.1016/j.patter.2022.100468.
[76] F. Tao et al., “Digital twin and blockchain enhanced smart
manufacturing service collaboration and management,” J Manuf Syst,
vol. 62, pp. 903–914, 2022, doi:
https://doi.org/10.1016/j.jmsy.2020.11.008.
[77] L. Stan, A. F. Nicolescu, C. Pupăză, and G. Jiga, “Digital Twin and
web services for robotic deburring in intelligent manufacturing,” J
Intell Manuf, 2022, doi: 10.1007/S10845-022-01928-
X/FULLTEXT.HTML.
[78] A. Asvadi, A. Mitriakov, C. Lohr, and P. Papadakis, “Digital Twin
Driven Smart Home: A Feasibility Study,” Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics), vol. 13287 LNCS, pp. 18–29,
2022, doi: 10.1007/978-3-031-09593-1_2/FULLTEXT.HTML.
[79] C. Zhuang, Z. Liu, J. Liu, H. Ma, S. Zhai, and Y. Wu, “Digital Twin-
based Quality Management Method for the Assembly Process of
Aerospace Products with the Grey-Markov Model and Apriori
Algorithm,” Chinese Journal of Mechanical Engineering (English
Edition), vol. 35, no. 1, Dec. 2022, doi: 10.1186/S10033-022-00763-
8/FULLTEXT.HTML.
[80] L. Wang, T. Deng, Z.-J. M. Shen, H. Hu, and Y. Qi, “Digital twin-
driven smart supply chain,” Frontiers of Engineering Management,
vol. 9, no. 1, pp. 56–70, Mar. 2022, doi: 10.1007/S42524-021-0186-9.
[81] M. Braun and J. Krutzinna, “Digital twins and the ethics of health
decision-making concerning children,” Patterns, vol. 3, no. 4, 2022,
doi: 10.1016/j.patter.2022.100469.
[82] G. S. Saini, A. Fallah, P. Ashok, and E. van Oort, “Digital Twins for
Real-Time Scenario Analysis during Well Construction Operations,”
Energies (Basel), vol. 15, no. 18, 2022, doi: 10.3390/en15186584.
[83] P. Chaber et al., “Digital Twins in the Practice of High-Energy Physics
Experiments: A Gas System for the Multipurpose Detector,” Sensors,
vol. 22, no. 2, 2022, doi: 10.3390/s22020678.
[84] A. Sharma, E. Kosasih, J. Zhang, A. Brintrup, and A. Calinescu,
“Digital Twins: State of the art theory and practice, challenges, and
open research questions,” J Ind Inf Integr, vol. 30, p. 100383, 2022,
doi: https://doi.org/10.1016/j.jii.2022.100383.
[85] S. T. Mulder et al., “Dynamic Digital Twin: Diagnosis, Treatment,
Prediction, and Prevention of Disease During the Life Course,” J Med
Internet Res, vol. 24, no. 9, 2022, doi: 10.2196/35675.
[86] E. A. Stahlberg et al., “Exploring approaches for predictive cancer
patient digital twins: Opportunities for collaboration and innovation,”
Front Digit Health, vol. 4, 2022, doi: 10.3389/fdgth.2022.1007784.
[87] G. Coorey et al., “The health digital twin to tackle cardiovascular
disease—a review of an emerging interdisciplinary field,” NPJ Digit
Med, vol. 5, no. 1, 2022, doi: 10.1038/s41746-022-00640-7.
[88] L. Kasper, F. Birkelbach, P. Schwarzmayr, G. Steindl, D. Ramsauer,
and R. Hofmann, “Toward a Practical Digital Twin Platform Tailored
to the Requirements of Industrial Energy Systems,” Applied Sciences
(Switzerland), vol. 12, no. 14, 2022, doi: 10.3390/app12146981.
[89] A. Nasirahmadi and O. Hensel, “Toward the Next Generation of
Digitalization in Agriculture Based on Digital Twin Paradigm,”
Sensors, vol. 22, no. 2, 2022, doi: 10.3390/s22020498.
[90] M. R. Valero, B. J. Hicks, and A. Nassehi, “A Conceptual Framework
of a Digital-Twin for a Circular Meat Supply Chain,” Lecture Notes in
Mechanical Engineering, pp. 188–196, 2023, doi: 10.1007/978-3-031-
18326-3_19/FULLTEXT.HTML.
[91] J. Cai, J. Chen, Y. Hu, S. Li, and Q. He, “Digital twin for healthy
indoor environment: A vision for the post-pandemic era,” Frontiers of
Engineering Management, Mar. 2023, doi: 10.1007/S42524-022-
0244-Y.
[92] K. Riaz, M. McAfee, and S. S. Gharbia, “Management of Climate
Resilience: Exploring the Potential of Digital Twin Technology, 3D
City Modelling, and Early Warning Systems,” Sensors, vol. 23, no. 5,
2023, doi: 10.3390/s23052659.
[93] D. Zhong, Z. Xia, Y. Zhu, and J. Duan, “Overview of predictive
maintenance based on digital twin technology,” Heliyon, vol. 9, no. 4,
2023, doi: 10.1016/j.heliyon.2023.e14534.
[94] R. Ward et al., “The challenges of using live-streamed data in a
predictive digital twin,” J Build Perform Simul, 2023, doi:
10.1080/19401493.2023.2187463.
[95] H. N. Rafsanjani and A. H. Nabizadeh, “Towards digital architecture,
engineering, and construction (AEC) industry through virtual design
and construction (VDC) and digital twin,” Energy and Built
Environment, vol. 4, no. 2, pp. 169–178, 2023, doi:
10.1016/j.enbenv.2021.10.004.
[96] Y. Li and W. Zhang, “Traffic flow digital twin generation for highway
scenario based on radar-camera paired fusion,” Sci Rep, vol. 13, no. 1,
Dec. 2023, doi: 10.1038/S41598-023-27696-Z/FULLTEXT.HTML.
[97] M. Spiryagin, J. Edelmann, F. Klinger, and C. Cole, “Vehicle system
dynamics in digital twin studies in rail and road domains,” Vehicle
System Dynamics, 2023, doi: 10.1080/00423114.2023.2188228.
[98] J. Ferré-Bigorra, M. Casals, and M. Gangolells, “The adoption of
urban digital twins,” Cities, vol. 131, 2022, doi:
10.1016/j.cities.2022.103905.
[99] Y. Sun, R. Rameezdeen, C. Chow, and J. Gao, “Integrating BIM and
IoT for Digital Twin Platform in Building Operation Management:
Opportunities and Challenges,” in ICCREM 2022: Carbon Peak and
Neutrality Strategies of the Construction Industry - Proceedings of the
International Conference on Construction and Real Estate
Management 2022, W. Y., L. S., and S. G.Q.P., Eds., STEM, Univ. of
South AustralisSA, Australia: American Society of Civil Engineers
(ASCE), 2022, pp. 284–292. doi: 10.1061/9780784484562.031.
[100] J. Golovatchev, G. Kirchgebsner, A. Bezlakovskii, and E.
Bezalkovskii, “Digital Twins for the Basalt Fiber Production 4.0:
Smart Digitalization in the Fiber Industry through Industrial IoT
(IIoT),” in 2021 International Conference on Engineering
Management of Communication and Technology, EMCTECH 2021 -
Proceedings, INCOTELOGY GmbH, Pulheim, 50259, Germany:
Institute of Electrical and Electronics Engineers Inc., 2021. doi:
10.1109/EMCTECH53459.2021.9619180.
[101] S. F. R. Alves, A. Uribe-Quevedo, D. Chen, J. Morris, and S.
Radmard, “Developing a VR Simulator for Robotics Navigation and
Human Robot Interactions employing Digital Twins,” Ontario Tech
University, Software Informatics Research Centre, Canada: Institute
of Electrical and Electronics Engineers Inc., 2022, pp. 121–125. doi:
10.1109/VRW55335.2022.00036.
[102] R. Geng, M. Li, Z. Hu, Z. Han, and R. Zheng, “Digital Twin in smart
manufacturing: remote control and virtual machining using VR and
AR technologies,” Structural and Multidisciplinary Optimization, vol.
65, no. 11, Nov. 2022, doi: 10.1007/S00158-022-03426-3.
[103] A. Arsiwala, F. Elghaish, and M. Zoher, “Digital twin with Machine
learning for predictive monitoring of CO2 equivalent from existing
buildings,” Energy Build, vol. 284, 2023, doi:
10.1016/j.enbuild.2023.112851.
[104] G. C. Doubell, A. H. Basson, K. Kruger, and P. D. F. Conradie, “A
Digital Twin System to Integrate Data Silos in Railway
Infrastructure,” in Studies in Computational Intelligence, Springer
Science and Business Media Deutschland GmbH, 2023, pp. 142–153.
doi: 10.1007/978-3-031-24291-5_12.
[105] S. M. E. Sepasgozar, “Differentiating Digital Twin from Digital
Shadow: Elucidating a Paradigm Shift to Expedite a Smart,
Sustainable Built Environment,” Buildings, vol. 11, no. 4, p. 151, Apr.
2021, doi: 10.3390/buildings11040151.
