AI-Powered Digital Twins for Enhancing Strategic Decision-Making in Smart Manufacturing Systems
DOI:
https://doi.org/10.52171/herald.277Keywords:
Digital Twin, Smart Manufacturing, Artificial Intelligence, Decision Support, Hybrid Modeling, Simulation, Industrial EngineeringAbstract
In the era of smart manufacturing, decision-making processes face increasing complexity due to dynamic environments, data overload, and system interconnectivity. Digital Twin technology, which enables the creation of virtual replicas of physical assets, has emerged as a vital tool to address these challenges. When enhanced with Artificial Intelligence, Digital Twins can analyze data patterns, predict system behaviors, and support strategic decisions in real time. This paper proposes a hybrid modeling framework that integrates simulation-based Digital Twins with AI algorithms for improved operational performance and decision accuracy. The conceptual model is illustrated through a hypothetical smart factory case, highlighting its potential to reduce response time, optimize resource allocation, and improve system adaptability. The findings offer a foundation for further exploration of intelligent decision-support systems in digitalized production environments.
References
1. Boschert, S., Rosen, R. (2016). Digital twin the simulation aspect. In Mechatronic Futures (pp. 59–74). Springer. https://doi.org/10.1007/978-3-319-32156-1_5
2. Gabor, T., Belzner, L., Kiermeier, M., Beckert, B., Schuppert, A. (2016). A simulation-based architecture for smart cyber-physical systems. Simulation Modelling Practice and Theory, 60, 1–16. https://doi.org/10.1016/j.simpat.2015.08.003
3. Grieves, M., Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85–113). Springer.
4. Herwig, S., Glaessgen, E., Stargel, D. (2020). Deep reinforcement learning for decision-making in manufacturing systems. Journal of Manufacturing Systems, 56, 310–325. https://doi.org/10.1016/j.jmsy.2020.06.002
5. Jones, D., Snider, C., Nassehi, A., Yon, J., Hicks, B. (2020). Characterizing the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002
6. Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
7. Lee, J., Bagheri, B., Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
8. Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L. (2020). Industrial Artificial Intelligence for smart manufacturing: Methodologies and applications. IEEE Transactions on Industrial Informatics, 17(4), 2964–2975. https://doi.org/10.1109/TII.2020.3013937
9. Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L. (2022) Deep reinforcement learning-based real-time scheduling in smart manufacturing. Computers & Industrial Engineering, 167, 107991. https://doi.org/10.1016/j.cie.2022.107991
10. Lu, Y., Liu, C., Wang, K. I. K., Huang, H., Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837.
https://doi.org/10.1016/j.rcim.2019.101837
11. Madni, A.M., Madni, C.C., Lucero, S.D. (2019). Leveraging digital twin technology in model-based systems engineering. Systems, 7(1), 7.
https://doi.org/10.3390/systems7010007
12. Moyne, J., Qamsane, Y., Balta, E., Kovalenko, I., Faruque, A. (2020). A requirements-driven digital twin framework: Specification and case study. Applied Sciences, 10(18), 6486. https://doi.org/10.3390/app10186486
13. Opresnik, D., Taisch, M. (2015). The value of big data in servitization. International Journal of Production Economics, 165, 174–184 https://doi.org/10.1016/j.ijpe.2014.12.036
14. Qi, Q., Tao, F., Hu, T., & Anwer, N. (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58, 3–21.
https://doi.org/10.1016/j.jmsy.2020.06.017
15. Srai, J. S., Lorentz, H. (2019). Developing resilient supply networks: A digital twin approach. International Journal of Operations & Production Management, 39(1), 109–138. https://doi.org/10.1108/IJOPM-03-2017-0196
16. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935–3953. https://doi.org/10.1080/00207543.2018.1443229
17. Uhlemann, T. H.-J., Lehmann, C., Steinhilper, R. (2017). The digital twin: Realizing the cyber-physical production system for Industry 4.0. Procedia CIRP, 61, 335–340. https://doi.org/10.1016/j.procir.2016.11.152
18. Zhang, H., Zhang, G., Lai, K.-K., Wang, S. (2021). Multi-objective decision support for energy-efficient flexible manufacturing using a digital twin. Journal of Cleaner Production, 283, 124633. https://doi.org/10.1016/j.jclepro.2020.124633
19. Zhang, Y., Tao, F. (2019). Optimization methods for decision-making systems in smart manufacturing: A review. Complex & Intelligent Systems, 5, 263–280. https://doi.org/10.1007/s40747-018-0092-3
20. Məmmədova, A.M., Zhilkişbaeva G.S., Məmmədova A.N. (2025). Süni İntellekt (AI) və əşyaların internetinin (IoT) inteqrasiyası. Azərbaycan Mühəndislik Akademiyasının Xəbərləri 17 (4): 87-93. https://doi.org/10.52171/herald.332
21. Hajiyev Y.M., Dadashov F.H. (2025). Artificial Intelligence and Digital Twin in Airport Operations. Scientific Journal. Vol. 27, №3, pp. 69-77. 10.30546/EMNAA.2025.25.03.115
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Elvin Nasirov
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
