Sarcouncil Journal of Applied Sciences Aims & Scope

Abstract: Modern supply chains operate in environments characterized by high data velocity, structural complexity, and increasing uncertainty across supplier, logistics, and distribution networks. Traditional enterprise architectures built on centralized data warehouses and batch-based workflows are increasingly unable to support real-time decision-making and predictive optimization. As a result, organizations experience delayed responses, fragmented insights, and reduced operational resilience. This paper proposes a Cognitive Enterprise Data Architecture (CEDA) as a unified framework for enabling AI-driven supply chain optimization. The architecture integrates data fabric automation, semantic knowledge graphs, and machine learning pipelines into a closed-loop decision ecosystem. Unlike conventional systems that treat data processing and analytics as separate stages, the proposed framework embeds intelligence directly into operational workflows, transforming raw data into actionable decisions. The study introduces a six-stage architecture comprising data acquisition, semantic integration, intelligent modeling, predictive analytics, orchestration, and execution. Mathematical and system-level models are presented for predictive risk estimation, dynamic optimization, and continuous learning. The framework supports near real-time synchronization across distributed enterprise systems, enabling proactive response to disruptions and operational variability. From an engineering management perspective, this research reframes enterprise data architecture as a decision-centric infrastructure, where performance improvements arise from reduced decision latency and enhanced coordination across organizational units. The findings demonstrate how cognitive architectures improve forecasting accuracy, reduce operational costs, and enhance supply chain responsiveness. The proposed model provides both theoretical foundations and enterprise implementation guidance for AI-enabled supply chain systems.