AI-enabled Design for Resilience and Adaptability in Concurrent Engineering Education
DOI:
10.29303/jaie.v2i2.1885Published:
2026-10-21Downloads
Abstract
The many interdependencies of product, process, manufacturing, supply and life cycle decisions in a concurrent engineering product development process can also increase the risk of operational disruptions due to the speed of development and the high degree of interdependency. Requirements change; supplier volatility, component obsolescence, cyber-physical defects, regulatory changes, and environmental risks can cascade quickly through interacting design decisions. This paper proposes a more inclusive conceptual model for the introduction of the integration of AI for design of resilience and adaptability within the context of concurrent engineering education. It maintains five core principles, which are linked directly and closely to fundamental capacities for resilience: Modularity, Redundancy, Flexibility, Feedback and Learning, and Collaborative Networks; and links these to five core capacities: Anticipation, Absorption, Recovery, Adaptation, and Learning. Additionally, the paper differentiates between resilience and reliability, robustness, flexibility and adaptability; introduces a life cycle risk-to-design workflow; and clarifies the role of digital twins, configuration management, change-impact analysis and cross-functional governance in providing evidence-based educational and industrial responses. The analysis does not consider resilience as a product attribute, but rather as a property of a system – one that is created by the interplay of technical architecture, organizational coordination, the relationships with suppliers, information quality and recovery procedures. The manuscript draws from a comprehensive review of the latest published literature and contains a conceptual integration and an executable validation plan to inform future research and engineering education at different points in the life cycle and in complex industries.
Keywords:
Concurrent engineering Resilience engineering Adaptable design Modularity Redundancy Flexibility Change propagation Digital twins Lifecycle engineering Supply-chain riskReferences
Ciano, C., Chrszon, P., Fischer, P. M., Amato, F., & Gerndt, A. (2025, April). Adaptive AI in concurrent engineering: a paradigm shift in design and integration. In International Conference on Advanced Information Networking and Applications (pp. 19-30). Cham: Springer Nature Switzerland.
Alli, Y.A., Anuar, H., Manshor, M.R., Okafor, C.E., Kamarulzaman, A.F., Akçakale, N., Nazeri, F.N.M., Bodaghi, M., Suhr, J. and Nasir, N.A.M., 2024. Optimization of 4D/3D printing via machine learning: A systematic review. Hybrid Advances, 6, p.100242.
Holzinger, A., Müller, H., & Stjepandić, J. (2026). Human-Centric, AI-Enabled Concurrent Engineering: Towards a Cross-Domain Framework. Journal of Industrial Information Integration, 101192.
Lin, Z., Tran, N. K., & Babar, M. A. (2026, June). Towards AI-Enabled Engineering of Digital Twins: An Architecture-Centric Approach. In 2026 IEEE 23rd International Conference on Software Architecture (ICSA) (pp. 229-236). IEEE.
Maldonado-Romo, J., Bradley, R., Anthony, B., Juárez, L., Yoon, J., Amador, A., Guerrero, E., Bergmann, N., Montesinos, L. and Ponce, P., 2026. A collaborative and sustainable engineering design framework enabled by agentic AI. International Journal of Sustainable Engineering, 19(1), p.2624166.
Devgan, A. (2026, February). Powering the AI Supercycle: Design for AI and AI for Design. In 2026 IEEE International Solid-State Circuits Conference (ISSCC) (Vol. 69, pp. 26-34). IEEE.
Shinde, P. K., Ananthan, S., Thangam, D., Booshan, B., Booshan, S., & Patil, S. (2026). Ethics by Design: Embedding Responsibility in AI-Enabled Product Development. In Rethinking Responsibility at the Intersection of AI and Corporate Liability (pp. 173-196). IGI Global Scientific Publishing.
Sharbaf, M. S. (2026, April). Integrating Artificial Intelligence Into System Security Engineering Design: Enhancing Efficiency And Security. In 2026 IEEE International Systems Conference (SysCon) (pp. 1-6). IEEE.
Ekengwu, I.; Jude, D.; Ilechukwu, A.; Anyaora, S. C.; Mmadumere, A. U. (2026). Machine Learning for Process Optimization and Defect Detection in Metal Additive Manufacturing: A Critical Review of Algorithms, In-Situ Monitoring Strategies, and Quality Assurance Frameworks. Journal of Mechanical Engineering and Manufacturing, 2 (4), 22.
Onwurah, U. O., Ani, C. S., Anyaora, S. C., & Obiafudo, O. J. (2026). A Machine Learning Evaluation of Machinery Vibration Signals: A Comparative Study. Journal Innovation in Information and Computer Technology, 3(2), 99-108.
Ekaterina, G., & Alina, V. (2026, April). AI-Driven Cross-Cultural Engineering Education: A Human-Centered Adaptive Learning Framework. In 2026 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-10). IEEE.
Mohamad, M., Saali, T., Laryeafio, M. N., Ayertey, S., & Nyame, D. (2026). The impact of AI adoption on engineers’ job satisfaction and organisational culture: a systematic literature review and roadmap for engineering education. Journal of Engineering Education Transformations, 39(3 [special issue]), 79-90.
Hosseinzadeh, A., & Taghipour, S. (2025). Strategic framework for resilient manufacturing: Concurrent product design and supplier selection. International Journal of Systems Science: Operations & Logistics, 12(1), 2490595.
Donelli, G., & Nagel, B. (2025). Systems engineering holistic approach for aircraft, manufacturing and supply chain concurrent design (Doctoral dissertation, University of Naples).
Wang, P., Youn, B. D., & Hu, C. (2013, August). Concurrent design of functional reliability and failure prognosis for engineered resilience. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 55881, p. V03AT03A036). American Society of Mechanical Engineers.
Rosso, C. B., & Saurin, T. A. (2018). The joint use of resilience engineering and lean production for work system design: A study in healthcare. Applied ergonomics, 71, 45-56.
Firoozi, A. A., Firoozi, A. A., Alashker, Y., & Ahmad, S. (2024). Advancing civil engineering: The transformative impact of neuromorphic computing on infrastructure resilience and sustainability. Results in Engineering, 24, 103487.
Rubio-Romero, J. C., Pardo-Ferreira, M. D. C., Rojas, M. M., López-Arquillos, A., & Suarez-Cebador, M. (2018, September). Resilience engineering: Concepts of the new paradigm. In Engineering Digital Transformation: Proceedings of the 11th International Conference on Industrial Engineering and Industrial Management (pp. 133-140). Cham: Springer International Publishing.
Abdelmoumen, A., Benzadri, Z., & Rodriguez, I. B. (2026). Comprehensive Exploration of Resilience and Adaptability in IoT-Based System-of-Systems: Bibliometric and Full-Text Literature Analysis. In European Conference on Software Architecture (pp. 243-258). Springer, Cham.
Wang, T., Xu, T. Z., Ding, Z., Sha, M., Ye, L., Tang, J., ... & Wan, C. (2026). Resilience and Adaptability Analysis of Port-Centric Transport Networks for Meteorological Disasters: A Case of Shanghai Port. Journal of Marine Science and Engineering, 14(11), 1034.
Senapati, M. K., Pradhan, C., Padmanaban, S., & Al Zaabi, O. (2025). Photovoltaic MPPT performance adaptability to partial shading resilience and load variations with modified adaptive Jaya optimization. IEEE Transactions on Consumer Electronics, 71(1), 734-747.
Fatima, E., Singh, A., Dahiya, P., & Bhusal, N. (2026). Building a Resilient Workforce in the AI Era. In Large Language Models, AI, and Workforce Transformation (pp. 100-130). Productivity Press.
