From Predictive to Prescriptive Supply Chains: The Strategic Role of Artificial Intelligence in End-to-End Optimization

This study examined the strategic transition from predictive to prescriptive artificial intelligence (AI) in achieving end-to-end supply chain optimization. Drawing on dynamic capabilities theory, the research investigated how predictive analytics capability (PAC) and prescriptive analytics capability (PrAC) influenced supply chain agility, resilience, and cost efficiency, while considering the mediating role of end-to-end integration. A quantitative research design was employed using survey data collected from 312 supply chain and operations managers. Structural equation modeling was applied to test the proposed relationships. The results indicated that PAC significantly influenced agility (β = 0.41, p < 0.001) and resilience (β = 0.36, p < 0.001). However, PrAC demonstrated stronger effects on resilience (β = 0.53, p < 0.001), agility (β = 0.47, p < 0.001), and cost efficiency (β = 0.49, p < 0.001). End-to-end integration partially mediated these relationships, increasing explanatory power to R² = 0.58 for resilience and R² = 0.52 for agility. The findings suggested that predictive analytics functioned primarily as a sensing capability, whereas prescriptive analytics acted as a decision-optimization mechanism that enhanced responsiveness and disruption recovery. The study contributed to the growing literature on AI-enabled supply chains by empirically validating the superior strategic value of prescriptive intelligence in dynamic and uncertain environments. The results provided managerial guidance for organizations seeking to advance from forecasting-based systems to AI-driven optimization platforms for sustainable competitive advantage.