Supply Chain Resilience Metrics: Optimization under Multi-Risk Shocks

Background: Modern supply chains are exposed to multi-risk shocks that interact across demand, supply, logistics, finance, and cyber domains. These interactions challenge resilience scorecards that rely on isolated indicators.Methods: This review synthesizes definitions of supply chain resilience, operational resilience metrics, and robust and risk-averse optimization frameworks. We develop a measurement-to-decision pipeline that links stress scenarios to policy choices.Results: We identify a compact and operationally meaningful metric set—time-to-survive (TTS), time-to-recover (TTR), service continuity, and cost-to-serve—and show how these metrics can be embedded in robust, minimax, and CVaR-based optimization formulations. We describe how structured multi-risk stress scenarios translate metrics into decisions on inventory buffers, backup capacity, sourcing diversification, and logistics routing.Conclusions: Conclusions: Resilience metrics become operationally useful when tied to explicit decision levers and validated through structured stress tests, not treated as stand-alone scorecards.