Quantitative Decision Models for Enhancing Supply Chain Resilience in the Face of Global Disruptions
Keywords:
Supply Chain Resilience, Predictive Analytics, Quantitative Decision Models, Risk Management, Operational Agility, Data-Driven Strategies, Machine Learning, Global Disruptions, Empirical AnalysisAbstract
The growing frequency of global disruptions has challenged traditional supply chain paradigms, necessitating robust decision-making models. This study undertakes a quantitative analysis utilizing advanced multivariate techniques and machine learning algorithms to assess the resilience of supply chains across various industries during unprecedented crises. A comprehensive dataset comprising historical disruption cases is analyzed, revealing critical success factors and vulnerabilities inherent to supply chains. Our findings indicate that predictive analytics and dynamic risk assessment frameworks significantly enhance operational resilience. The study concludes with actionable insights for practitioners seeking to fortify supply chains against future disruptions, with implications for strategic management in a volatile economic landscape.
References
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