Applied Sciences
| Open Access | Designing Viable Supply Chains for High‑Tech Manufacturing: Integrating Resilience, Agility, and Regulatory Constraints in a Geopolitical Era
Arjun Mehta , Institute for Global Supply Chain Studies, University of Transnational Logistics, New Delhi, IndiaAbstract
The accelerating complexity of global supply chains, particularly in high‑technology sectors such as semiconductor and GPU manufacturing, brings profound challenges in managing risk, uncertainty, and regulatory disruption. This paper develops a comprehensive conceptual framework for building “viable supply chains,” defined as supply networks capable of sustaining performance under geopolitical turbulence, regulatory constraints, trade‑policy shifts, and demand volatility. Drawing upon established literature on supply chain risk management (Fan & Stevenson, 2018; Ho et al., 2015), supply chain resilience and agility (Gligor et al., 2019; Han, Chong & Li, 2020; Hosseini, Ivanov & Dolgui, 2019), and recent analyses of supply‑chain strategies in the semiconductor industry (Bernstein, 2023; Lulla, 2025; BIS, 2023; Flamm & Bonvillian, 2025), the framework synthesizes prior conceptualizations and extends them to address contemporary challenges such as reshoring, capacity reservation under disruption, and elasticity of substitution between domestic and imported goods (Ahmad & Riker, 2020; Devarajan, Go & Robinson, 2023). Using a systematic literature-based methodology, we analyze key dimensions—risk identification and mitigation, supply chain agility, resilience capacities, contractual and sourcing strategies, regulatory compliance, and design for adaptability. The results highlight critical capabilities required for supply‑chain viability in high‑tech manufacturing: diversified sourcing including backup and reshored suppliers; dynamic coordination and information flows; contractual mechanisms for revenue sharing under uncertainty; and alignment with regulatory and trade policy frameworks. The discussion elaborates theoretical implications, limitations, and proposes directions for empirical validation and extension, including digital‑twin simulations (Ivanov & Dolgui, 2021) and performance metric benchmarking (Han, Chong & Li, 2020). This integrative framework provides a roadmap for academics and practitioners seeking to design, analyze, and adapt supply chains for strategic robustness in a rapidly evolving geopolitical landscape.
Keywords
Supply chain viability, resilience; agility, high‑tech manufacturing, reshoring
References
Fan, D., Yeung, A. C. L., Tang, C. S., Lo, C. K. Y., & Zhou, Y. (2022). Global operations and supply‑chain management under the political economy. Journal of Operations Management, 68(8), 816–823. https://doi.org/10.1002/joom.1232
Fan, Y., & Stevenson, M. (2018). A review of supply chain risk management: definition, theory, and research agenda. International Journal of Physical Distribution and Logistics Management, 48(3), 205–230. https://doi.org/10.1108/IJPDLM-01-2017-0043
Gligor, D., Gligor, N., Holcomb, M., & Bozkurt, S. (2019). Distinguishing between the concepts of supply chain agility and resilience: A multidisciplinary literature review. International Journal of Logistics Management, 30(2), 467–487. https://doi.org/10.1108/IJLM-10-2017-0259
Han, Y., Chong, W. K., & Li, D. (2020). A systematic literature review of the capabilities and performance metrics of supply chain resilience. International Journal of Production Research, 4541–4566. https://doi.org/10.1080/00207543.2020.1785034
Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review.
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285–307. https://doi.org/10.1016/j.tre.2019.03.001
Hou, J., Zeng, A. Z., & Sun, L. (2017). Backup sourcing with capacity reservation under uncertain disruption risk and minimum order quantity. Computers and Industrial Engineering, 103, 216–226. https://doi.org/10.1016/j.cie.2016.11.011
Hu, B., & Feng, Y. (2017). Optimization and coordination of supply chain with revenue sharing contracts and service requirement under supply and demand uncertainty. International Journal of Production Economics, 183, 185–193. https://doi.org/10.1016/j.ijpe.2016.11.002
International Organization for Standardization. (2018). ISO 31000:2018 – Risk management – Guidelines. www.iso.org
Ivanov, D. (2022). Viable supply chain model: integrating agility, resilience and sustainability perspectives — lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 319(1), 1411–1431. https://doi.org/10.1007/s10479-020-03640-6
Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning and Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450
Lulla, K. (2025). Reshoring GPU production: testing strategy adaptations for US‑based factories. International Journal of Applied Mathematics, 38(10s), 2411–2440.
Ahmad, A., & Riker, D. (2020). Updated Estimates of the Elasticity of Substitution Between Domestic and Imported Goods. U.S. International Trade Commission.
Bernstein, J. (2023). Supply Chain Priorities for Chemical Products in Semiconductor Manufacturing: What’s in It for Material Companies—A Review of CHIPS Act, Inflation Reduction Act (IRA), and Tax Incentives. Semiconductor Equipment and Materials International (SEMI) and American Chemistry Council.
Bureau of Industry and Security (BIS). (2023). Commerce strengthens restrictions on advanced computing semiconductors, semiconductor manufacturing equipment, and supercomputing items to countries of concern. U.S. Department of Commerce.
Devarajan, S., Go, D. S., & Robinson, S. (2023). Trade Elasticities in Aggregate Models: Estimates for 191 Countries. World Bank Group, Development Economics Prospects Group.
Flamm, K., & Bonvillian, W. B. (2025). Solving America’s Chip Manufacturing Crisis. Issues in Science and Technology, 41, 26–31.
Download and View Statistics
Copyright License
Copyright (c) 2025 Arjun Mehta

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.

