A Decision Analytics Pipeline for Balancing Business, Environmental and Social Impacts of Supply Chain Disruptions

Supply chain resilience is essential for maintaining global economic stability and ensuring the continuous delivery of goods and services. This study introduces a new decision analytics framework to stress test supply chains by improving robustness, addressing network vulnerabilities, and developing adaptive mitigation strategies. The proposed approach includes a modifed Time to Survive (TTS) model, a sustainable mitigation planning Time to Recover (TTR) model, and a sequential Monte Carlo simulation to measure variability in mitigation plans. It focuses on reducing business, environmental, and social impacts while supporting strategies such as dual sourcing and capacity reallocation. Multi-objective mathematical programming and open-source technologies are used to develop the framework. Computational experiments with a synthetic supply chain network generator confirm its scalability, efficiency, and practical value.