Integrative Framework to Enhance Supply-Chain Resilience through Advanced Forecasting, Anomaly Detection, and Optimized Resource Allocation

Global supply chains face increasing complexity and vulnerability to disruptions, necessitating more robust management approaches. This study evaluates the effectiveness of artificial-intelligence (AI) technologies in strengthening supply-chain resilience via improved prediction capabilities and automated response mechanisms. We investigate three critical dimensions—predictive accuracy, disruption detection, and dynamic resource allocation—within an integrated AI framework. The framework achieves a mean absolute percentage error (MAPE) of 4.5 % in demand forecasting, promoting stable inventory management and reducing stockouts and overstock. Anomaly detection attains 88 % sensitivity with a 7 % false-positive rate, enabling early interventions that cut downtime by 12 % and lower disruption-related costs by 9 %. Finally, the dynamic resource-allocation model reduces disruption-related expenses by 16 % and shortens lead times during demand surges by 17–21 %. These results demonstrate that embedding AI into supply-chain management delivers a robust, adaptive approach to operational stability, equipping supply chains to navigate demand volatility and unforeseen disruptions more effectively.