Comparative Analysis of AI-Driven Risk Prediction Methods in Retail Supply Chain Disruption Management: A Multi-Enterprise Study

Abstract

Supply chain disruptions have become increasingly problematic for retail organizations in recent years, particularly given the compounding effects of pandemic-related shocks, geopolitical instabilities, and climate-driven events. This paper reports on an eighteen-month field study examining how fifteen retail enterprises—ranging from small regional operators to multinational corporations—have deployed artificial intelligence technologies for risk prediction and mitigation. We find that deep learning approaches, specifically long short-term memory networks, achieve prediction accuracies around 85% for demand fluctuations during crisis periods, though this figure masks considerable variation across contexts. Interestingly, smaller enterprises utilizing cloud-based platforms report cost reductions averaging 15%, challenging conventional assumptions about scale advantages in technology adoption. Our analysis employs a mixed-methods approach combining quantitative performance metrics with qualitative insights from 45 executive interviews. The findings suggest that organizational factors, particularly data governance maturity and change management practices, may be equally important as algorithmic sophistication in determining implementation outcomes. We contribute to the literature by providing empirically-grounded frameworks for technology selection while acknowledging the limitations inherent in generalizing from case-based research.