Supply Chain 4.0: The Role of Artificial Intelligence in Enhancing Resilience and Operational Efficiency

Abstract

The proliferation of Artificial Intelligence (AI) promises transformative potentials for supply chain management (SCM), yet empirical evidence on realized supply chain performance gains remains fragmented and context-dependent. This article presents a comprehensive conceptual investigation into how AI-driven innovations interact with traditional supply chain management practices to influence supply chain performance, resilience, and long-term value creation. Drawing exclusively on a curated selection of literature — spanning empirical studies on SCM practices and performance, machine‑learning applications in demand forecasting, and critical analyses of AI adoption barriers — this research identifies recurring patterns, tensions, and open questions. The analysis reveals that while AI-enabled capabilities (e.g., demand forecasting, supplier scouting, logistics optimization) can significantly augment supply chain responsiveness and resilience under dynamism (Belhadi et al., 2021; Bottani et al., 2019; Gao & Feng, 2023), their effectiveness is highly mediated by data quality, organizational readiness, integration scope, and governance (SupplyChainBrain, 2019). Traditional supply chain practices remain foundational: empirical studies continue to show that SCM practices contribute significantly to performance, whereas strategy alone often proves a weak predictor (Sukati et al., 2012). The paper concludes by proposing a conceptual integrative framework that maps prerequisites for effective AI‑SCM synergy, outlines potential trade‑offs, and suggests directions for future empirical research to validate and refine the framework.