Global supply networks, once designed for maximum efficiency and Just-in-Time (JIT) delivery, are now shown to be highly vulnerable due to the escalating polycrisis of geopolitical instability, climatic disruptions, and pandemics. This vulnerability results in significant costs: a recent study estimates total losses exceeding $2.3 trillion in global production during major disruptions, such as ElectroLean Inc.’s disastrous $1.2 billion failure during Southeast Asian floods. This study addresses systemic vulnerability by proposing and experimentally validating a transformational framework: Antifragile Supply Chain Management (A-SCM). A-SCM is a six-pillar system designed to actively gain strength from instability, going beyond simple resilience (recovery). We demonstrate how combining Strategic Redundancy and Optionality, Enhanced Visibility and Sensing, Decentralization and Modularity, Adaptive Capacity, Ecosystem Collaboration and Trust, and Continuous Learning and Stress Testing enables organizations to not only withstand shocks but also turn them into engines for innovation and competitive advantage. Case evidence highlights its effectiveness: while vulnerable JIT systems collapse, A-SCM practitioners like MediTech Global turned the Suez Canal blockage into a €85 million EBITDA gain through strategic near-shoring and ecosystem flexibility. Implementing this approach requires reevaluating metrics—such as adopting Mean Time To Improve (MTTI) and Optionality Value—and creating environments that reward smart risk-taking. This study offers a comprehensive framework for this vital transformation, exploring pathways, challenges, and sectoral adjustments. The evidence is clear: in the tumultuous early 21st century, survival depends on moving beyond fragile efficiency. Embracing antifragility is a critical strategic shift—turning disruptions into lasting competitive advantages, structural improvements, and ongoing innovation.
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- Publication year
2025
- Venue
Frontiers in Research
- Publication date
2025-07-23
- Fields of study
Not labeled
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- External record
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Semantic Scholar
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