Digital Twin–Driven Lifecycle Engineering for Sustainable Industrial Ecosystems
Keywords:
Digital Twin, Lifecycle Engineering, Industrial Internet of Things, Sustainable Manufacturing, Circular Economy, Cyber-Physical Systems, Socio-Technical Infrastructure.Abstract
The transition toward a circular economy and carbon neutrality requires a fundamental reconfiguration of industrial production, moving from linear value chains to complex, self-optimizing ecosystems. This paper proposes a comprehensive framework for Digital Twin-Driven Lifecycle Engineering (DT-LCE), serving as a systemic catalyst for sustainable industrial development. Digital twins—dynamic, high-fidelity virtual representations of physical assets—have evolved beyond simple monitoring tools to become the foundational infrastructure for predictive maintenance, resource optimization, and end-of-life management. By synthesizing advances in cyber-physical systems, large-scale artificial intelligence, and socio-technical theory, this research explores the structural trade-offs between computational fidelity and operational scalability. We investigate the multi-layered architecture required to facilitate seamless data orchestration across the product lifecycle, from initial design and procurement to operational service and ultimate decommissioning. The discussion emphasizes the critical role of governance and policy in standardizing digital threads, ensuring data sovereignty, and promoting equitable access to advanced manufacturing technologies. Furthermore, the paper analyzes the implications of digital twins for systemic robustness, highlighting how virtual simulations can mitigate the risks of stochastic supply chain disruptions. Through an interdisciplinary lens, we argue that the long-term sustainability of industrial ecosystems depends on the successful integration of digital twins into a broader socio-technical framework that prioritizes ecological integrity, transparency, and human-centric design.
References
1.Abadi, M., et al. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security.
2.Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
3.Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
4.Chen, B., et al. (2018). Smart factory of Industry 4.0: Key technologies, application case, and challenges. IEEE Access, 6.
5.Dietterich, T. G. (2017). Steps toward robust artificial intelligence. AI Magazine, 38(3).
6.Ellen MacArthur Foundation (2015). Towards a circular economy: Business rationale for an accelerated transition.
7.Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
8.Glaessgen, E. H., & Stargel, D. S. (2012). The digital twin paradigm for future NASA and US Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference.
9.Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Bending Resilience in Complex Systems. In Transdisciplinary Perspectives on Complex Systems. Springer.
10.Heppelmann, J. E., & Porter, M. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11).
11.Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786).
12.IEEE (2019). Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems.
13.Kagermann, H., et al. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Acatech.
14.Kusiak, A. (2018). Smart manufacturing must embrace big data. Nature, 544(7648).
15.LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553).
16.Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3.
17.Liao, Y., et al. (2017). Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12).
18.Monostori, L. (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. Procedia CIRP, 17.
19.NIST (2020). Four Principles of Explainable Artificial Intelligence. Draft NISTIR 8312.
20.O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
21.Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
22.Rosen, R., et al. (2015). About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, 48(3).
23.Schleich, B., et al. (2017). Shaping the digital twin for design and production engineering. CIRP Annals, 66(1).
24.Schwab, K. (2017). The Fourth Industrial Revolution. Currency.
25.Tao, F., et al. (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4).
26.Wang, L., et al. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37.
27.Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. MIT Press.
28.Zhong, R. Y., et al. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5).
29.Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
Downloads
Published
How to Cite
Issue
Section
License
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
