Published on: August 2026
INTEGRATING FKF SPECTRAL ANALYSIS, ARTIFICIAL INTELLIGENCE, AND DIGITAL TWINS FOR SUPPLY-CHAIN RESILIENCE
Shinde S M Shubham S Aayushee G
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Abstract
Keywords— Distributional FKF Transform; Spectral Analysis; Supply-Chain Resilience; Intelligent Supply Chains; Multi-Echelon Supply Chains; Disruption Detection; Artificial Intelligence; Digital Twins; Trusted Data Exchange; Sustainable Logistics; Quantum Optimization; Industry 5.0..
How to Cite this Paper
M, S. S., S, S. & G, A. (2026). Integrating FKF Spectral Analysis, Artificial Intelligence, and Digital Twins for Supply-Chain Resilience. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(8), 1-9. https://doi.org/10.55041/ijcope.v2i8.270
M, Shinde, et al.. "Integrating FKF Spectral Analysis, Artificial Intelligence, and Digital Twins for Supply-Chain Resilience." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i8.270.
M, Shinde,Shubham S, and Aayushee G. "Integrating FKF Spectral Analysis, Artificial Intelligence, and Digital Twins for Supply-Chain Resilience." International Journal of Creative and Open Research in Engineering and Management 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i8.270.
References
- SM Harle,“Artificial Intelligence for Supply Chain Risk Prediction and Mitigation: A Systematic Review, Conceptual Framework, and Future Research Agenda,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 8, pp. 70–80, July 2026.
- SM Harle, “Artificial Intelligence-Driven Framework for Village-Level Water Conservation and Groundwater Sustainability,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 8, pp. 31–49, July 2026.
- Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846.
- Ivanov, D. (2021). Digital supply chain twins: Managing the ripple effect, resilience, and disruption risks by data-driven optimization, simulation, and visibility. In Handbook of Ripple Effects in the Supply Chain (pp. 309–330). Springer.
- Ghanem, R. G., & Spanos, P. D. (1991). Stochastic Finite Elements: A Spectral Approach. Springer-Verlag
- Harle, SM, & et al (2024, August). Advancing seismic resilience: Focus on building design techniques. In Structures (Vol. 66, p. 106432). Elsevier.
- M. Harle et al., “Artificial Intelligence Approaches for Strength Prediction and Optimization of Composite Concrete Mixtures: A Systematic Review and Future Research Framework,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 8, pp. 81–97, July 2026.
- M. Harle et al., “Artificial Intelligence-Based Pavement Crack Detection: A Comprehensive Review of Machine Learning and Deep Learning Techniques,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 8, pp. 50–69, July 2026.
- M. Harle et al., “Digital Twins and Artificial Intelligence for Sustainable Textile Raw Material Processing,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 8, pp. 14–30, July 2026.
- Heese, R., et al. (2026). Hybrid Quantum-Classical Optimization for Multi-Objective Supply Chain Logistics. arXiv preprint arXiv:2602.05364.
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- •Published on: Aug 31 2026
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