The section citations connect domain mechanisms, methods and cases to the underlying sources. Read each reference for its question, design, findings and limits. Technical guides, case investigations and scholarly articles do not provide interchangeable forms of evidence.
- L01 · Peer-reviewed research
Christopher, M.; Peck, H. (2004). Building the Resilient Supply Chain. International Journal of Logistics Management 15(2), 1–14.
Original publication / source ↗Network visibility, collaboration and resilient design.
- L02 · Peer-reviewed research
Tang, C.S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics 103, 451–488.
Original publication / source ↗Operational and disruption risks; coordination and flexibility strategies.
- L03 · Peer-reviewed research
Kleindorfer, P.R.; Saad, G.H. (2005). Managing Disruption Risks in Supply Chains. Production and Operations Management 14, 53–68.
Original publication / source ↗Integration of risk assessment and mitigation.
- L04 · Peer-reviewed research
Pettit, T.J.; Fiksel, J.; Croxton, K.L. (2010). Ensuring Supply Chain Resilience: Development of a Conceptual Framework. Journal of Business Logistics 31(1), 1–21.
Original publication / source ↗Balance between vulnerabilities and capabilities.
- L05 · Peer-reviewed research
Pettit, T.J.; Croxton, K.L.; Fiksel, J. (2013). Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool. Journal of Business Logistics 34(1), 46–76.
Original publication / source ↗Structured assessment; interpretation depends on organizational context.
- L06 · Peer-reviewed research
Pettit, T.J.; Croxton, K.L.; Fiksel, J. (2019). The Evolution of Resilience in Supply Chain Management: A Retrospective on Ensuring Supply Chain Resilience. Journal of Business Logistics 40(1), 56–65.
Original publication / source ↗Evolution of resilience concepts and research priorities.
- L07 · Peer-reviewed research
Ponomarov, S.Y.; Holcomb, M.C. (2009). Understanding the concept of supply chain resilience. International Journal of Logistics Management 20(1), 124–143.
Original publication / source ↗Preparedness, response and adaptive capability.
- L08 · Peer-reviewed research
Lee, H.L.; Padmanabhan, V.; Whang, S. (1997). Information Distortion in a Supply Chain: The Bullwhip Effect. Management Science 43(4), 546–558.
Original publication / source ↗Demand-signal amplification; distinguish it from physical disruption propagation.
- L09 · Peer-reviewed research
Croson, R.; Donohue, K. (2006). Behavioral Causes of the Bullwhip Effect and the Observed Value of Inventory Information. Management Science 52(3), 323–336.
Original publication / source ↗Behavioral ordering and information-sharing experiments.
- L10 · Peer-reviewed research
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 136, 101922.
Original publication / source ↗Early-pandemic simulation; scenario results are not observed forecasts.
- L11 · Peer-reviewed research
Hosseini, S.; Ivanov, D.; Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E 125, 285–307.
Original publication / source ↗Quantitative methods, resilience capacities and methodological limits.
- L12 · Peer-reviewed research
Li, Y.; Chen, K.; Collignon, S.; Ivanov, D. (2021). Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability. European Journal of Operational Research 291, 1117–1131.
Original publication / source ↗Direction and propagation of network disruptions.
- L13 · Peer-reviewed research
Dolgui, A.; Ivanov, D.; Sokolov, B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research 56, 414–430.
Original publication / source ↗Structural disruption propagation and recovery policies.
- L14 · Peer-reviewed research
Verschuur, J.; Koks, E.E.; Hall, J.W. (2022). Ports’ criticality in international trade and global supply-chains. Nature Communications 13, 4351.
Original publication / source ↗Trade-weighted port criticality and network substitution.
- L15 · Peer-reviewed research
Verschuur, J.; Koks, E.E.; Hall, J.W. (2023). Systemic risks from climate-related disruptions at ports. Nature Climate Change 13, 804–806.
Original publication / source ↗Shared climate exposure and maritime network consequences.
- L16 · Peer-reviewed research
Verschuur, J.; Koks, E.E.; Hall, J.W. (2021). Global economic impacts of COVID-19 lockdown measures stand out in high-frequency shipping data. PLOS ONE 16, e0248818.
Original publication / source ↗AIS-derived trade proxies; maritime flows are not the whole economy.
- L17 · Official technical assessment
UNCTAD (2024). Navigating troubled waters: Impact to global trade of disruption of shipping routes in the Red Sea, Black Sea and Panama Canal. UNCTAD/OSG/INF/2024/2.
Original publication / source ↗Dated rapid assessment of interacting chokepoint disruptions.
- L18 · Official investigation
NTSB (2025). Contact of Containership Dali with Francis Scott Key Bridge and Subsequent Bridge Collapse. Marine Investigation Report MIR-25-40.
Original publication / source ↗Final investigation; use final findings instead of superseded preliminary causal speculation.
- L19 · Official assessment
UNDRR (2019). Chapter 2: Systemic risks, the Sendai Framework and the 2030 Agenda. Global Assessment Report on Disaster Risk Reduction.
Original publication / source ↗Includes Thailand manufacturing disruption and systemic dependencies.
- L20 · Official case collection
UNDRR (2013). Private sector strengths applied: good practices in disaster risk reduction from Japan. Case collection.
Original publication / source ↗Documented private-sector responses to the 2011 Great East Japan disaster.
- L21 · Official data and report
World Bank (2023). Connecting to Compete 2023: Trade Logistics in an Uncertain Global Economy. Logistics Performance Index.
Original publication / source ↗Benchmarking logistics; national scores do not establish the status of one corridor.
- L22 · Peer-reviewed research
Guan, D. et al. (2020). Global supply-chain effects of COVID-19 control measures. Nature Human Behaviour 4, 577–587.
Original publication / source ↗Input-output scenario modeling and its time-resolution limits.
- C01 · peer-reviewed
Rinaldi, S. M.; Peerenboom, J. P.; Kelly, T. K. (2001). Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Systems Magazine, 21(6), 11–25.
Original publication / source ↗Distinguishes physical, cyber, geographic and logical dependencies; use the categories to formulate a case-specific dependency hypothesis.
- C02 · peer-reviewed
Pescaroli, G.; Alexander, D. (2018). Understanding Compound, Interconnected, Interacting, and Cascading Risks: A Holistic Framework. Risk Analysis, 38, 2245–2257.
Original publication / source ↗Provides distinctions for examining interacting risks and propagation; a plausible pathway still needs evidence in the case.
- C03 · peer-reviewed
Saltelli, A. et al. (2010). Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications, 181, 259–270.
Original publication / source ↗Methodological reference for sensitivity analysis; assumptions about independence and the model structure must accompany the chosen estimator.
- C04 · peer-reviewed
Wilkinson, M. D. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.
Original publication / source ↗Guides metadata and reuse. Accessible data may still have legitimate authorization and protection requirements.
- C05 · standard
W3C (2013). PROV-DM: The PROV Data Model. W3C Recommendation, 30 April 2013.
Original publication / source ↗Represents provenance through entities, activities and agents; useful for connecting a result to its transformations and responsible contributors.
- C06 · technical guide
JCGM (2008). Evaluation of measurement data — Supplement 1 to the GUM — Propagation of distributions using a Monte Carlo method. JCGM 101:2008.
Original publication / source ↗A methodological basis for propagating input distributions through a specified model, rather than treating simulated frequency as an observed event probability.
- C07 · peer-reviewed
Ostrom, E. (2009). A general framework for analyzing sustainability of social-ecological systems. Science, 325, 419–422.
Original publication / source ↗Supports a structured inquiry into actors, resources, institutions and interactions; institutional interpretation must be adapted to the system studied.
External sources are cited for research and learning. Their inclusion implies no partnership, certification or institutional endorsement.