在地緣政治衝突與自然災害風險日益頻繁下，全球供應鏈之中斷已從單一、短期事件，轉變為跨區域、多節點連鎖傳導的動態過程，使企業在採購決策上面臨高度不確定性與營運壓力。既有供應鏈管理文獻多以靜態或單一風險架構分析採購策略，鮮少同時納入雙重風險交互作用下之供應鏈結構與動態決策行為。本研究從品牌廠的採購策略視角出發，實務上，品牌廠主要向代工廠採購最終成品，然而其跨期交付能力與成本，實際高度受制於代工廠及更上游關鍵材料的供應狀態。當上游網絡供應商節點因外部風險產生中斷時，其產能折減與恢復速度的不確定性，將沿著供應鏈影響品牌廠的營運存貨與採購風險。
本研究以A公司之顯示卡供應鏈為研究個案，結合代理人基礎模型（Agent-based Model, ABM）與動態規劃（Dynamic Programming, DP），建構一反映地緣政治與自然災害風險之多階層供應鏈模擬結構，透過ABM模擬供應鏈各節點在外部風險衝擊下的產能折減與恢復過程，並於每期以DP模擬採購決策，據以分析在不確定環境中的採購策略表現。本研究比較四類採購策略：單一採購、雙重採購、備援採購I與備援採購II，在雙重動態風險下的獲利表現與服務水準差異，並透過敏感度分析，探討當外部環境風險持續升高時，原先具優勢之採購策略是否仍能維持其績效表現，抑或產生策略選擇上的轉換。
模擬結果顯示，在地緣政治與自然災害風險的交互作用下，採購策略績效表現取決於在供應受限後的即時調整與備援來源啟用能力，隨著風險加劇，單一採購策略在極端嚴峻情境下，其績效波動增加且服務水準表現不佳，優勢策略轉向具彈性之備援採購I與備援採購II，顯示過度依賴單一來源或維持固定採購比例之策略，於主要供應來源受到產能限制與風險加劇時，難以即時調整，導致利潤與服務水準出現顯著下滑。
本研究揭示，採購策略之設計並非一成不變，而是應隨著外部環境動態調整，本研究之結果亦可協助企業在雙重風險交互作用的情境下，掌握採購策略設計之決策邏輯，以作為採購策略制定之參考。

As geopolitical tensions and natural disaster risks have become increasingly prevalent, global supply chain disruptions have evolved from isolated, short-term events into dynamic processes characterized by cross-regional and multi-node ripple effects, placing substantial uncertainty and operational pressure on firms regarding sourcing strategies. Existing supply chain management literature predominantly examines procurement strategies under static or single-risk settings, and has paid limited attention to supply chain structures and dynamic decision-making behaviors under the interplay between geopolitical and natural disaster risks.
This study approaches the problem from a focal firm's sourcing strategy perspective. In practice, a focal firm primarily procures final products from contract manufacturers; however, its multi-period delivery capability and costs are substantially constrained by the supply conditions of these manufacturers and more upstream raw materials. When upstream network nodes experience disruptions due to external risks, the resulting uncertainties in capacity degradation and restoration velocity propagate along the supply chain structure, affecting the focal firm's operational inventory and sourcing risks.
This study adopts Company A’s graphics card supply chain as an empirical case and combines Agent-based Model (ABM) with Dynamic Programming (DP) to develop a multi-echelon supply chain simulation structure that reflects both geopolitical and natural disaster risks. The ABM is employed to simulate the process of capacity degradation and restoration of each node under external shocks, while the DP model optimizes procurement decisions in each period, thereby evaluating sourcing strategy performance under uncertainty. Four sourcing strategies, single sourcing, dual sourcing, backup sourcing I, and backup sourcing II, are compared in terms of their profit performance and service level outcomes under the interplay between geopolitical and natural disaster risks. Sensitivity analyses are further conducted to investigate whether sourcing strategies that perform well under low external risk conditions remain effective as the external environment risk continues to intensify, or whether strategic shifts become necessary.
The simulation results indicate that under the interplay of geopolitical and natural disaster risks, the performance of sourcing strategies critically depends on a firm’s ability to make real-time adjustments and activate backup sources following supply constraints. As risk levels escalate, the single sourcing strategy, which is advantageous under low-risk conditions, exhibits increased performance volatility and deteriorated service levels in extreme scenarios. Consequently, the strategy advantage shifts toward flexible backup sourcing I and backup sourcing II, demonstrating that strategies relying heavily on a single supplier or maintaining fixed sourcing allocations fail to adjust responsively when major supply sources face capacity constraints and intensifying risks, leading to a significant drop in both profits and service levels.
This study reveals that sourcing strategy design should not be static but rather dynamic and adaptive in response to the external environment. The insights generated from this research can assist firms in mastering the core decision logic of sourcing strategy design when facing compounded risks, thereby serving as a valuable reference for strategic procurement planning.

1. Allon, G., & Van Mieghem, J. A. (2010). Global Dual Sourcing: Tailored Base-Surge Allocation to Near- and Offshore Production. Management Science, 56(1), 110–124.
2. Bednarski, L., Roscoe, S., Blome, C., & Schleper, M. C. (2023). Geopolitical disruptions in global supply chains: A state-of-the-art literature review. Production Planning & Control, 36(4), 536–562.
3. Berthou, A., Criscuolo, C., Haramboure, A., Samek, L., & Chua, J. (2025). Tracking the risks in production networks: A focus on natural disasters (OECD Science, Technology and Industry Working Papers, No. 2025/19). OECD Publishing.
4. Bertsekas, D. P. (2005). The dynamic programming algorithm. In Dynamic Programming and Optimal Control (3rd ed.). Athena Scientific.
5. Caldara, D., & Iacoviello, M. (2022). Measuring Geopolitical Risk. The American Economic Review, 112(4), 1194–1225.
6. Chen, L.-M. (2025). Investigating the favorability of engaging a second source by supply chain buyers under geopolitical tensions and single-point-hit supply disruptions. Manuscript submitted for publication.
7. Chen, L.-M., & Chang, W.-L. (2021). Supply- and cyber-related disruptions in cloud supply chain firms: Determining the best recovery speeds. Transportation Research Part E: Logistics and Transportation Review, 151, 102347.
8. Chen, L.-M., Liu, Y. E., & Yang, S.-J. (2015). Robust supply chain strategies for recovering from unanticipated disasters. Transportation Research Part E: Logistics and Transportation Review, 77, 198–214.
9. Coe, N. M., & Yeung, H. W.-C. (2019). Global production networks: Mapping recent conceptual developments. Journal of Economic Geography, 19(4), 775–801.
10. D'Arcy, P., Norman, D. & Shan, Sh. (2012) Costs and Margins in the Retail Supply Chain. RBA Bulletin, June, 13-22.
11. Darom, N. A., Hishamuddin, H., Ramli, R., & Mat Nopiah, Z. (2018). An inventory model of supply chain disruption recovery with safety stock and carbon emission consideration. Journal of Cleaner Production, 197, 1011–1021.
12. Datta, P.P., Christopher, M. & Allen, P. (2007). A Leading Journal of Supply Chain Management Agent-Based Modelling of Complex Production / Distribution Systems to Improve Resilience. International Journal of Logistics Research: And Applications, 10(3), 187–203.
13. Dong, B., Tang, W., Zhou, C., & Ren, Y. (2021). Is dual sourcing a better choice? The impact of reliability improvement and contract manufacturer encroachment. Transportation Research Part E: Logistics and Transportation Review, 149, 102275.
14. Dong, L., & Kouvelis, P. (2020). Impact of tariffs on global supply chain network configuration: Models, predictions, and future research. Manufacturing & Service Operations Management, 22(1), 25–35.
15. Ellram, L. M., & Ueltschy Murfield, M. L. (2019). Supply chain management in industrial marketing—Relationships matter. Industrial Marketing Management, 79, 36–45.
16. Gong, X., Chao, X., & Zheng, S. (2014). Dynamic pricing and inventory management with dual suppliers of different lead times and disruption risks. Production and Operations Management, 23(12), 2058–2074.
17. Gölgeci, I., Yildiz, H. E., & Andersson, U. (2020). The rising tensions between efficiency and resilience in global value chains in the post-COVID-19 world. Transnational Corporations, 27(2), 127–141.
18. Gurtu, A. (2021). Optimization of inventory holding cost due to price, weight, and volume of items. Journal of Risk and Financial Management, 14(2), 65.
19. He, J., Alavifard, F., Ivanov, D., & Jahani, H. (2019). A real-option approach to mitigate disruption risk in the supply chain. Omega: The International Journal of Management Science, 88, 133–149.
20. Henderson, J., Dicken, P., Hess, M., Coe, N., & Yeung, H. W.-C. (2002). Global production networks and the analysis of economic development. Review of International Political Economy, 9(3), 436–464.
21. Herold, D. M., & Marzantowicz, Ł. (2023). Supply chain responses to global disruptions and its ripple effects: an institutional complexity perspective. Operations Management Research, 16, 2213–2224.
22. Holgado, M., & Niess, A. (2023). Resilience in global supply chains: analysis of responses, recovery actions and strategic changes triggered by major disruptions. Supply Chain Management: An International Journal, 28(6), 1040–1059.
23. 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.
24. Hou, Y., Wang, X., Wu, Y. J., & He, P. (2018). How does trust affect the topology of supply chain network and its resilience? An agent-based approach. Transportation Research Part E: Logistics and Transportation Review, 116, 229–241.
25. Hu, X., Gurnani, H., & Wang, L. (2013). Managing risk of supply disruptions: Incentives for capacity restoration. Production and Operations Management, 22(1), 137–150.
26. Ivanov, D. (2017). Simulation-based single vs dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management, 11(1), 24-43.
27. Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922.
28. 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.
29. Ivanov, D., & Das, A. (2020). Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note. International Journal of Integrated Supply Management, 13(1), 90–102.
30. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915.
31. Ivanov, D., Pavlov, A., Dolgui, A., Pavlov, D., & Sokolov, B. (2016). Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies. Transportation Research Part E: Logistics and Transportation Review, 90, 7–24.
32. Ivanov, D., & Sokolov, B. (2019). Simultaneous structural-operational control of supply chain dynamics and resilience. Annals of Operations Research, 283(1), 1191–1210.
33. Macal, C., & North, M. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162.
34. Mafakheri, F., Breton, M., & Ghoniem, A. (2011). Supplier selection-order allocation: a two-stage multiple criteria dynamic programming approach. International Journal of Production Economics, 132(1), 52–57.
35. Mei, Z., Xu, M., Luo, S., Tan, Y., & Li, H. (2022). Concrete formwork reuse in a supply chain with dynamic changes using ABMS and discrete events. Journal of Cleaner Production, 332, 130038.
36. Moradlou, H., Skipworth, H., Bals, L., Aktas, E., & Roscoe, S. (2025). Geopolitical Disruptions and Supply Chain Structural Ambidexterity. International Journal of Operations & Production Management, 45(4), 836–862.
37. Neilson, J., Pritchard, B., & Yeung, H. W.-C. (2014). Global value chains and global production networks in the changing international political economy: An introduction. Review of International Political Economy, 21(1), 1–8.
38. Ni, N., Howell, B. J., & Sharkey, T. C. (2018). Modeling the impact of unmet demand in supply chain resiliency planning. Omega: The International Journal of Management Science, 81, 1–16.
39. Paul, S. K., Sarker, R., & Essam, D. (2017). A quantitative model for disruption mitigation in a supply chain. European Journal of Operational Research, 257(3), 881–895.
40. Powell, W. B. (2009). What you should know about approximate dynamic programming. Naval Research Logistics (NRL), 56(3), 239–249.
41. Rafiei, M., Mohammadi, M., & Torabi, S. A. (2013). Reliable multi period multi product supply chain design with facility disruption. Decision Science Letters, 2(2), 81–94.
42. Raj, A., Mukherjee, A. A., de Sousa Jabbour, A. B. L., & Srivastava, S. K. (2022). Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned. Journal of Business Research, 142, 1125–1139.
43. Rasshyvalov, D., Portnov, Y., Sigaieva, T., Alboshchii, O., & Rozumnyi, O. (2024). Navigating geopolitical risks: Implications for global supply chain management. Multidisciplinary Reviews, 7, 1-8.
44. Roscoe, S., Aktas, E., Petersen, K. J., Skipworth, H. D., Handfield, R. B., & Habib, F. (2022). Redesigning global supply chains during compounding geopolitical disruptions: the role of supply chain logics. International Journal of Operations & Production Management, 42(9), 1407–1434.
45. Roscoe, S., Skipworth, H., Aktas, E., & Habib, F. (2020). Managing supply chain uncertainty arising from geopolitical disruptions: evidence from the pharmaceutical industry and Brexit. International Journal of Operations & Production Management, 40(9), 1499–1529.
46. Rozhkov, M., Ivanov, D., Blackhurst, J., Nair, A. (2022). Adapting supply chain operations in anticipation of and during the COVID-19 pandemic. Omega: The International Journal of Management Science, 110, 102635.
47. Sawik, T. (2021). On the risk-averse selection of resilient multi-tier supply portfolio. Omega: The International Journal of Management Science, 101, 102267.
48. Schmitt, A. J., & Singh, M. (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1), 22–32.
49. Sheffi, Y., & Rice, J. B., (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41–48.
50. Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., Gusikhin, O., Sanders, M., & Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375–390.
51. Sturgeon, T. J., & Kawakami, M. (2011). Global value chains in the electronics industry: characteristics, crisis, and upgrading opportunities for firms from developing countries. International Journal of Technological Learning, Innovation and Development, 4(1-3), 120–147.
52. Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103, 451–488.
53. Tomlin, B. (2006). On The Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks. Management Science, 52(5), 639–657.
54. Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 79, 22–48.
55. Yeung, H. W.-c., & Coe, N. M. (2015). Toward a Dynamic Theory of Global Production Networks. Economic Geography, 91(1), 29–58.
56. Zhu, M., Miao, S., Lam, H. K. S., Liang, C., & Yeung, A. C. L. (2025). Navigating through geopolitical risk: the role of supply chain concentration. International Journal of Operations & Production Management, 45(5), 1032–1065.