Artificial Intelligence and Supply Chain Resilience: A Three-Stage Framework for Prediction, Response, and Adaptive Learning

Abstract

Heightened exposure of global supply chains to systemic shocks (e.g., geopolitical tensions and climate-related disruptions). These “black swan” events keep repeating, highlighting the urgent need for more resilience to keep operations going. The improvement of the resilience of the supply chain has become an important method for logistics enterprises, including manufacturing and circulation enterprises, to resist operational risks and maintain long-term stable operation. Although existing literature recognises the potential of AI in various individual supply chain functions, there is a notable lack of systematic integration of AI in the canonical three-stage resilience framework of prediction, response, and recovery/learning. Moreover, practical constraints such as data scarcity and algorithmic opacity are generally ignored. This paper uses the method of systematic literature review, integrates and sorts out the relevant cutting-edge research results at home and abroad, analyses the differentiated empowerment paths of artificial intelligence in each stage of supply chain resilience at different levels, proposes supporting optimisation solutions for various technical limitations and fills in the gap of phased integrated research in the current field. This research contributes to the discourse on digital resilience by reconciling the transformative potential of AI with its inherent limitations. The framework offers actionable insights for managers who seek to balance algorithmic efficiency and human oversight and ethical governance in volatile global markets.