Optimising Supply Chain Resilience and Efficiency Using Artificial Intelligence in Response to Disruptions
Michael Kwakye Agyapong
ABSTRACT
This study investigates the role of Artificial Intelligence (AI) in enhancing supply chain resilience and efficiency during disruptions. Drawing on the Dynamic Capabilities Theory, it evaluates how AI-driven demand forecasting, predictive analytics, and optimization algorithms support supply chain adaptability, continuity, and performance in the face of crises such as pandemics and geopolitical instabilities. Using a quantitative research design, data were collected from 228 firms in Pittsburgh, Pennsylvania, all of which have adopted AI in their operations. The findings reveal that AI-driven demand forecasting (β = 0.771), AIpowered predictive analytics (β = 0.091), and AI-based optimization algorithms (β = 0.083) significantly and positively influence supply chain efficiency during disruptions. Factor analysis confirmed the strong construct validity of the model, while regression analysis demonstrated a high explanatory power (R² = 0.878). This research contributes a novel, holistic framework that integrates technological, organizational, and human factors,
offering actionable insights for businesses aiming to build resilient and agile supply chains. It further recommends adopting advanced AI tools, fostering cross-functional collaboration, and implementing continuous monitoring to sustain long-term supply chain performance.
