Mapping the Future of Human Digital Twin Adoption in Job-shop Industries: A Strategic Prioritization Framework

Samiran Sardar; Nasif Morshed; Shezan Ahmed

doi:10.38032/scse.2026.4.108

Authors

Samiran Sardar Department of Industrial Engineering and Management, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
Nasif Morshed Department of Industrial Engineering and Management, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
Shezan Ahmed Department of Industrial Engineering and Management, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh

DOI:

https://doi.org/10.38032/scse.2026.4.108

Keywords:

Human Digital Twin, Job-Shop Industries, Fuzzy AHP-TOPSIS, Use-Case Prioritization, Industry 5.0

Abstract

Although Digital Twin is actively deployed in manufacturing, its human-centric counterpart - Human Digital Twin (HDT) is understudied, especially in job-shop production with high task variability and manual labor. HDT applications like ergonomic posture monitoring, fatigue prediction and health-based task assignment offer benefits to industries in emerging economies. However, poor digital maturity, lack of awareness and doubts about use-case applicability hinder adoption. This study provides a strategic prioritization framework to aid human-centric digital evolution in labor-intensive industries for guiding the selection of HDT applications delivering the highest value with the lowest implementation threshold. An integrated Fuzzy AHP-TOPSIS approach evaluates the use-cases based on criteria like implementation cost, technological maturity, scalability. These criteria and use-cases were identified based on input from a five-member expert panel and verified for consistency (CR < 0.1). Analysis shows posture monitoring and fatigue prediction as most influential and practicable, especially in semi-digital environments. Strengths include compliance with Industry 5.0 principles incorporating technology and human factors. Lack of field validation and subjective knowledge pose drawbacks. Future work should include simulation-based validation and pilot tests on real job-shop settings. Ultimately, the research offers a decision-support system helping industries balance innovativeness and practicability in early stage of HDT adoption.

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References

[1] M. Attaran and B. G. Celik, “Digital Twin: Benefits, use cases, challenges, and opportunities,” Decis. Anal. J., vol. 6, p. 100165, Mar. 2023. DOI: https://doi.org/10.1016/j.dajour.2023.100165

[2] Y. Lin et al., “Human digital twin: a survey,” J. Cloud Comput., vol. 13, no. 1, p. 131, Aug. 2024. DOI: https://doi.org/10.1186/s13677-024-00691-z

[3] B. Gaffinet, J. Al Haj Ali, Y. Naudet, and H. Panetto, “Human Digital Twins: A systematic literature review and concept disambiguation for industry 5.0,” Comput. Ind., vol. 166, p. 104230, Apr. 2025. DOI: https://doi.org/10.1016/j.compind.2024.104230

[4] C. L. Colaw et al., “Methodology for Enablement of Human Digital Twins for Quality Assurance in the Aerospace Manufacturing Domain,” Sensors, vol. 25, no. 11, p. 3362, May 2025. DOI: https://doi.org/10.3390/s25113362

[5] E. Zio and L. Miqueles, “Digital twins in safety analysis, risk assessment and emergency management,” Reliab. Eng. Syst. Saf., vol. 246, p. 110040, Jun. 2024. DOI: https://doi.org/10.1016/j.ress.2024.110040

[6] A. Greco, M. Caterino, M. Fera, and S. Gerbino, “Digital Twin for Monitoring Ergonomics during Manufacturing Production,” Appl. Sci., vol. 10, no. 21, p. 7758, Nov. 2020. DOI: https://doi.org/10.3390/app10217758

[7] Y. You, Y. Liu, and Z. Ji, “Human Digital Twin for Real-Time Physical Fatigue Estimation in Human-Robot Collaboration,” in 2024 IEEE International Conference on Industrial Technology (ICIT), Bristol, United Kingdom: IEEE, Mar. 2024, pp. 1–6. DOI: https://doi.org/10.1109/ICIT58233.2024.10541029

[8] T. Sun, X. He, and Z. Li, “Digital twin in healthcare: Recent updates and challenges,” Digit. Health, vol. 9, p. 20552076221149651, Jan. 2023. DOI: https://doi.org/10.1177/20552076221149651

[9] M. Kuštelega, A. Shareef, and R. Mekovec, “The Role of Digital Twins in Future Skill Development,” 2024.

[10] Y. Liu, J. Feng, J. Lu, and S. Zhou, “A review of digital twin capabilities, technologies, and applications based on the maturity model,” Adv. Eng. Inform., vol. 62, p. 102592, Oct. 2024. DOI: https://doi.org/10.1016/j.aei.2024.102592

[11] S. Dittmann, P. Zhang, A. Glodde, and F. Dietrich, “Towards a scalable implementation of digital twins - A generic method to acquire shopfloor data,” Procedia CIRP, vol. 96, pp. 157–162, 2021. DOI: https://doi.org/10.1016/j.procir.2021.01.069

[12] L. Huang, M. Kapteyn, and K. E. Willcox, “Digital Twin: Graph Formulations for Managing Complexity and Uncertainty,” in 2024 IEEE Smart World Congress (SWC), Nadi, Fiji: IEEE, Dec. 2024, pp. 2141–2148. DOI: https://doi.org/10.1109/SWC62898.2024.00327

[13] F. Oettl, L. Eckart, and J. Schilp, “Cost estimation approach of a digital twin implementation in industry,” Procedia CIRP, vol. 118, pp. 318–323, 2023. DOI: https://doi.org/10.1016/j.procir.2023.06.055

[14] Mohd. Nazim, C. Wali Mohammad, and Mohd. Sadiq, “A comparison between fuzzy AHP and fuzzy TOPSIS methods to software requirements selection,” Alex. Eng. J., vol. 61, no. 12, pp. 10851–10870, Dec. 2022. DOI: https://doi.org/10.1016/j.aej.2022.04.005

[15] A. M. Paredes Rodríguez, J. J. Bravo Bastidas, J. C. Osorio Gómez, D. L. Peña Orozco, and J. González Feliu, “Fuzzy AHP TOPSIS Methodology for Multicriteria ABC Inventory Classification,” J. Eng., vol. 2023, pp. 1–11, Dec. 2023. DOI: https://doi.org/10.1155/2023/7661628

[16] Y. Upadhyay, F. Talib, S. A. Zaheen, and M. S. Ansari, “Industry 4.0 adoption framework in msmes using a hybrid fuzzy ahp-topsis approach,” Proc. Eng. Sci., vol. 5, no. 3, pp. 453–474, 2023. DOI: https://doi.org/10.24874/PES05.03.010