Analysis of supply chain resilience based on real-world datasets

Abstract

Background: Supply chain resilience (SCR) is critical for maintaining business continuity and competitive advantage in a global environment increasingly prone to disruptions like natural hazards and geopolitical tensions. However, a significant problem exists in the lack of standardised, data-driven metrics to compare and monitor vulnerabilities across different transport modes, which hinders effective risk mitigation.
Objectives: This study develops a roadmap for SCR in multimodal transport networks by identifying systemic logistical vulnerabilities. The research focuses on creating dimensionless resilience metrics that can support both real-time operational monitoring and long-term strategic decision-making.
Method: The research employs a quantitative, data-driven strategy. We analysed six real-world datasets – including aviation, maritime, rail, road and an Amazon last-mile delivery case study – through a rigorous pipeline of cleaning, contextual enrichment and indicator calculation.
Results: Findings reveal critical systemic risks: 21% of flights experience disruptions, and rail transport faces a 59% derailment rate. Navigation errors account for 60% of maritime incidents. Furthermore, the case study highlights those orders placed after 17:00 face a 70% delay rate, demonstrating a strong correlation between peak-hour traffic and delivery failure.
Conclusion: The study concludes that understanding the interplay between environmental factors and operational data is essential for proactive resilience.
Contribution: This article contributes a solid foundation for crisis simulation and strategic management. We recommend that logistics managers implement exact actions, such as adjusting delivery schedules based on temporal traffic density and weather forecasts, to meet resilience goals.