Digital twin: benefits, use cases, challenges, and opportunities
Open accessAbstract
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.
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Attaran et al. explained that the DT technique incorporates four technologies to acquire and store real-time data, extract details, and build a virtual model: XR, AI, IoT, and Storage (Cloud).
“But, Attaran et al. [48] explained that the DT technique incorporates a combination of four technologies to acquire and store real-time data, extract details to provide meaningful insights, and build a virtual model of an actual entity.The technologies above encompass the Extended Reality (XR), AI, IoT, and Storage (Cloud).”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [48]
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Digital twins and industrial internet of things: uncovering operational intelligence in Industry 4.0
Attaran, S., Attaran, M., & Celik, B. G. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100398 · Cited by 122 · see citing works
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Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26 · see citing works
“But, Attaran et al. [48] explained that the DT technique incorporates a combination of four technologies to acquire and store real-time data, extract details to provide meaningful insights, and build a virtual model of an actual entity.”
- Authors
- Attaran, M., & Celik, B. G.
- Affiliations
- California State University, Bakersfield (US)
- Corresponding author
- Attaran, Mohsen
- ORCID
- Attaran, Mohsen
- Venue
- Decision Analytics Journal
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 6
- Pages
- 100165
- ISSN
- 2772-6622 (ISSN-L)
- OpenAlex
- W4317632579
- Cited by
- 704
- References
- 64
- Citations by year
- 2026: 187 · 2025: 256 · 2024: 189 · 2023: 72
- FWCI
- 93.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Decision Analytics Journal (publishedVersion, cc-by)
- Roger Williams University - Digital Commons (Roger Williams University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Digital Innovation in Industries
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- digital twin, intelligent automation, industrial applications, enabling technologies, industrial adoption
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