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A Topic Modeling Approach to Determine Supply Chain Management Priorities Enabled by Digital Twin Technology

Author

Listed:
  • Enna Hirata

    (Graduate School of Maritime Sciences, Kobe University, Kobe 658-0022, Japan)

  • Daisuke Watanabe

    (Department of Logistics and Information Engineering, Tokyo University of Marine Science and Technology, Etchujima, Tokyo 135-8533, Japan)

  • Athanasios Chalmoukis

    (Department of Shipping, Trade and Transport, University of the Aegean, 811 00 Mitilini, Greece)

  • Maria Lambrou

    (Department of Shipping, Trade and Transport, University of the Aegean, 811 00 Mitilini, Greece)

Abstract

Background: This paper examines scientific papers in the field of digital twins to explore the different areas of application in supply chains. Methods: Using a machine learning-based topic modeling approach, this study aims to provide insights into the key areas of supply chain management that benefit from digital twin capabilities. Results: The research findings highlight key priorities in the areas of infrastructure, construction, business, technology, manufacturing, blockchain, and agriculture, providing a comprehensive perspective. Conclusions: Our research findings confirm several recommendations. First, the machine learning-based model identifies new areas that are not addressed in the human review results. Second, while the human review results put more emphasis on practicality, such as management activities, processes, and methods, the machine learning results pay more attention to macro perspectives, such as infrastructure, technology, and business. Third, the machine learning-based model is able to extract more granular information; for example, it identifies core technologies beyond digital twins, including AI/reinforcement learning, picking robots, cybersecurity, 5G networks, the physical internet, additive manufacturing, and cloud manufacturing.

Suggested Citation

  • Enna Hirata & Daisuke Watanabe & Athanasios Chalmoukis & Maria Lambrou, 2024. "A Topic Modeling Approach to Determine Supply Chain Management Priorities Enabled by Digital Twin Technology," Sustainability, MDPI, vol. 16(9), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3552-:d:1381728
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    References listed on IDEAS

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    1. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    2. Catherine Marinagi & Panagiotis Reklitis & Panagiotis Trivellas & Damianos Sakas, 2023. "The Impact of Industry 4.0 Technologies on Key Performance Indicators for a Resilient Supply Chain 4.0," Sustainability, MDPI, vol. 15(6), pages 1-31, March.
    3. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    4. Milena Kajba & Borut Jereb & Tina Cvahte Ojsteršek, 2023. "Exploring Digital Twins in the Transport and Energy Fields: A Bibliometrics and Literature Review Approach," Energies, MDPI, vol. 16(9), pages 1-23, May.
    5. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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