Cited by 878Digital Transformation in IndustryApplied Advanced TechnologiesBIM and Construction Integration

The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index; 42 of the 70 sources its claims cite were checked for support.

Digital Twin Technology Challenges and Applications: A Comprehensive Review

Open access

Abstract

A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This systematic literature review surveys digital twin (DT) technology across smart cities, freight logistics, medicine, engineering, and automotive domains, focusing on implementation challenges, enabling technologies, and maturity. It finds DT applications are still early-stage, with most at maturity level 3 or below and generally low societal readiness.

Key findings

  • The review selected 115 publications, retained 84 references, and analyzed 18 recent (2019–present) studies in depth for comparative analysis of enabling technologies, TRL, SRL, and maturity.
  • The global DT market was valued at USD $3.8 billion in 2019 and is expected to reach USD $35.8 billion by 2025, yet less than 5% of companies had something tangible at the time.
  • Most DT concepts in the literature are at maturity levels 0–3; Table 2 shows most applications at maturity level 3, with high TRL especially in smart cities and automotive, but all domains showing low societal readiness levels.
  • Five common implementation challenges are identified: data issues (trust, privacy, cybersecurity, governance, acquisition, large-scale analysis); lack of standards, frameworks, and regulations; high implementation costs; use of AI and big data for long-term/large-scale analysis; and communication network obstacles such as the need for 5G.

Methods

  • A systematic literature review (SLR) was conducted following Charles Sturt University guidelines and Cochrane Handbook principles, with research questions on DT state of the art, implementation challenges, Latin American limitations, and enabling technology trends.
  • Searches used ResearchGate, MDPI, Science Direct, and ProQuest; inclusion criteria required “digital twin” in keywords or title, publication from 2017 onward, and selection from Q1/Q2 Scopus-ranked journals across application industries.
  • From 115 collected publications, 84 references were retained, and 18 recent studies were analyzed in depth using a comparative table covering enabling technologies, TRL, SRL, maturity level, application industry, evaluation methods, physical twin, and sensors.

Limitations

  • The paper states five main DT implementation limitations: data-related issues (trust, privacy, cybersecurity, governance, acquisition, large-scale analysis); lack of standards, frameworks, and regulations; high costs from sensors and computational resources; challenges in using AI and big data for long-term/large-scale analysis; and communication network obstacles, including the need for faster interfaces such as 5G.
  • The paper also states that most DT applications have not reached high SRL, with relevant stakeholders often not identified or consulted and societal/environmental impact not well defined.
  • Evident from the text: the review is limited to publications from 2017 onward and mainly Q1/Q2 journals, with only 18 studies analyzed in depth; it is a literature synthesis without primary experimental validation, and the Latin American discussion notes greater challenges due to limited access to high-end technology in developing countries.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/rs14061335

Cites

71 of 71 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

42 of 70 cited sources checked · 19 supported · 2 flagged · accuracy not yet measured

Show all 40 claims

  • Probably not a substantial claim. 49
  • It may state two separate claims as one. 35
66 of 66
  • The paper relies on this without support.

    The 18 studies selected for final comparison are representative of digital twin implementation across the reviewed domains.

    “the final steps of our current work focused on gathering and comparing a small set of studies (18 for this work).”
    “These studies were compiled using every criterion in our methodology and are summarized in Table 2, where enabling technology trends, TRL, SRL and maturity levels are presented for each domain.”
  • The paper relies on this without support.

    The assigned TRL, SRL and maturity spectrum levels accurately characterize each study's development stage.

    “For evaluating different publications, a TRL, an SRL and a maturity spectrum index (only for implemented applications) were assigned.”
  • The paper relies on this without support.

    The reviewed literature is sufficiently comprehensive to identify the main challenges common to all domains.

    “From the reviewed literature, five main challenges that are common for all domains were found in DT technology implementation.”
  • The paper relies on this without support.

    Universal standards for DT technology lack widespread adoption.

    “Although universal standards for this technology still lack widespread adoption, the increasing amount of publications and attention towards DTs is an important step for standardization organizations such as ISO to take DTs into account.”
4 of 4

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Cited ISO-standard reference is a logistics paper

    Minor

    The text cites [47] for the description of ISO 19650-3:2020, but reference 47 is titled as a paper about a transportation-sector four-corner model.

  • Four-corner model is attributed to the wrong reference

    Minor

    The account attributes the four-corner model to [48], whose listed title concerns urban last-mile logistics. The listed four-corner-model paper is reference 47.

  • Viral-infection study is attributed to a health-standard reference

    Minor

    The text identifies [53] as a study simulating viral infections, but reference 53 is titled as a standardized digital-twin framework for health and well-being. The viral-infection paper appears as reference 52.

3 of 3

Citations

Cites

Flagged first, then by citations

Show all 70 references

Research works printed but not linked: 6

  1. Botin-Sanabria, D.M., Lozoya-Reyes, J.G., Vargas-Maldonado, R.C., Rodríguez-Hernández, K.L., Ramírez-Mendoza, R.A., Ramírez-Moreno, M.A., and Lozoya-Santos, J.J. (2021, January 3–5). Digital Twin for Urban Spaces: An Application. Proceedings of the International Conference on Industrial Engineering and Operations Management, Monterrey, Mexico.
  2. Quirk, 2020, Digital Twins: Details of Implementation: Part 2, AHRAE J., 62, 20
  3. Campos-Ferreira, A., Lozoya-Santos, J.J., Vargas-Martínez, A., Mendoza, R., and Morales-Menéndez, R. (2019). Digital Twin Applications: A review. Memorias del Congreso Nacional de Control Automático, Asociación de México de Control Automático.
  4. Quirk, 2020, Digital twins: Answering the hard questions, ASHRAE J., 62, 22
  5. Briggs, 2020, Deloitte Tech Trends 2020, Insights, 2020, 1
  6. Bottani, E., Assunta, C., Murino, T., and Vespoli, S. (2017, January 19–22). From the Cyber-Physical System to the Digital Twin: The process development for behaviour modelling of a Cyber Guided Vehicle in M2M logic. Proceedings of the Conference: XXII Summer School Francesco Turco–Industrial Systems Engineering, Paris, France.

Cited by

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Items 701–800 of 878

Metadata

Authors
Botín-Sanabria, D. M., Mihaita, A.-S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., & Lozoya-Santos, J. D. J.
Affiliations
  • Tecnológico de Monterrey (MX)
  • Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia
Corresponding author
Lozoya-Santos, Jorge de J.
Venue
Remote Sensing
Publisher
MDPI AG
Published
2022-03-09
Volume
14
Issue
6
Pages
1335
ISSN
2072-4292 (ISSN-L)
OpenAlex
W4220831070
Cited by
878
References
70
Citations by year
2026: 156 · 2025: 254 · 2024: 264 · 2023: 160 · 2022: 43
FWCI
82.36
Type
journal article
Language
en
Open access
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
  • Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
Keywords
digital twin, implementation barriers, data-driven decision making, product validation, simulation, industrial applications, automotive applications, medical applications, smart cities

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Cited sources are checked with a language model’s help (gpt-6-sol).

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