From Linear Logistics to Neural Supply Chains: Predictive Machine Learning and the Rise of Autonomous Supply Chain Intelligence

Abstract

Global supply chains are increasingly exposed to systemic volatility arising from geopolitical disruptions, demand uncertainty, and structural fragilities embedded in traditional linear logistics models. Conventional enterprise planning systems, grounded in deterministic forecasting and historical data extrapolation, have proven inadequate in environments characterized by rapid change and high complexity. This article reconceptualizes the contemporary supply chain as an intelligent, networked system driven by Predictive Machine Learning, Generative Artificial Intelligence, and autonomous decision architectures. Adopting a conceptual and industry-anchored analytical approach, the study synthesizes advances in feature-based forecasting, real-time data sensing, software-defined operations, and AI-enabled automation to explain the transition from reactive supply chain management toward anticipatory and self-healing logistics networks. Drawing on evidence from automotive and manufacturing ecosystems, the article illustrates how predictive analytics, generative models for unstructured data, and embodied AI systems jointly enhance end-to-end visibility, demand accuracy, inventory optimization, and operational resilience. The findings contribute to supply chain theory by reframing logistics as an adaptive intelligence system rather than a sequential flow of activities, while also offering practical insights for enterprises seeking strategic value from AI adoption. The study further highlights implications for emerging economies, particularly in the context of digital infrastructure and talent development. Overall, the article positions AI-driven supply chains as a foundational mechanism for achieving autonomous, resilient, and scalable global commerce.