AI-Driven Supply Chain Management Systems for Resilient and Sustainable Operations: The Role of Digital Twins, IoT, and Predictive Analytics

Abstract

Global supply chains are passing through a period of unusual volatility, in which lean, efficiency-first models have repeatedly failed to absorb systemic shocks such as pandemics and geopolitical disruption. This paper reviews how artificial intelligence (AI) and three of its closest enabling technologies, namely the Internet of Things (IoT), digital twins, and predictive analytics, are being used to build supply chains that are both more resilient and more sustainable. Drawing on peer-reviewed journal research published within the last five years, the study brings together streams of work that are usually examined separately and asks how they fit together within the emerging vision of Industry 5.0 and increasingly autonomous supply chains. The synthesis indicates that AI shifts supply chain management from a reactive posture to an anticipatory one. IoT supplies the real-time visibility that supports proactive risk management, predictive and prescriptive analytics turn that data into early warning of demand shifts and disruption and into recommended action, digital twins allow managers to stress-test recovery options before acting on them, and blockchain adds a layer of trust to the data exchanged between partners. The review finds that the value of these technologies appears when they are integrated into a coherent, layered architecture rather than adopted in isolation, and that their contribution to resilience runs through underlying capabilities such as visibility, flexibility, and collaboration rather than through the technology alone. It also examines the obstacles that continue to slow adoption, including implementation cost, cybersecurity exposure, a persistent digital skills gap, interoperability with legacy systems, and the limited interpretability of complex models. The paper proposes a layered reference architecture for autonomous supply chains and sets out recommendations for practitioners and policymakers seeking a technology-driven, human-centric, and environmentally responsible supply network.