Supply Chain Cognitive Decision Fabric: An Integrated AI Framework for Real-Time Autonomous Decision Intelligence in Enterprise Retail Operations

Abstract

Enterprise supply chains increasingly operate at a pace that outstrips human decision-making capacity. This article introduces the Supply Chain Cognitive Decision Fabric (CDF), a conceptual architecture that integrates machine learning, reinforcement learning, knowledge graphs, and large language models into a unified, layered AI decision system for enterprise retail operations. The CDF is organized across four functional layers (perception, reasoning, decision, and governance), enabling real-time inference from operational signals and the translation of those inferences into auditable, action-ready decisions. Drawing on recent advances in autonomous supply chain intelligence, generative AI integration, and explainability frameworks, the article presents the CDF as a response to the persistent gap between isolated AI tools and enterprise-wide decision coherence. Practitioner evidence from large-scale retail deployments illustrates the operational viability of each layer. The article concludes with implementation guidance, governance requirements, and a research agenda for federated and edge-native extensions of the cognitive decision fabric.