<![CDATA[The COVID-19 pandemic has exposed fundamental vulnerabilities in global supply chain systems, such as over-reliance on single suppliers and a lack of operational visibility. This has highlighted the urgent need for a new approach to risk management—one that leverages smart technologies. Artificial Intelligence (AI) has emerged as a promising solution, thanks to its capabilities in predictive analytics and adaptive, data-driven decision-making in real time. This study aims to develop an AI-based predictive system framework to enhance the resilience of global supply chains in the face of post-pandemic disruptions. Using the Design Science Research (DSR) methodology, the research designs and evaluates a system that integrates algorithms such as LSTM, Random Forest, Natural Language Processing (NLP), and Reinforcement Learning. It also applies a federated learning approach to ensure data privacy among supply chain partners. The study analyzes over 12,000 data entries from diverse sources, including IoT devices, weather data, demand trends, and social media. The system's effectiveness is evaluated through a combination of quantitative methods (PLS-SEM analysis on 103 respondents) and qualitative methods (interviews with 12 industry executives). The findings show that AI-driven predictive analytics significantly improve supply chain resilience (β = 0.67; p < 0.001), with demand forecasting accuracy increasing by up to 40% and delivery times reduced by 30%. Conceptually, the study contributes by designing a resilient model that integrates real-time visibility, adaptability, and cross-organizational collaborative learning. Unlike traditional approaches focused solely on automation, this framework offers a more holistic solution, addressing key gaps in the literature. The implication is clear: AI is becoming a strategic asset in building sustainable, resilient supply chains amid ongoing global uncertainty.]]>
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