Manufacturing environments increasingly depend on integrated operational data, interconnected production resources, and rapid responses to deviations in material availability, production capacity, quality, and delivery commitments. This paper develops a conceptual framework for AI-powered predictive exception management and supply network coordination in SAP S/4HANA manufacturing. The proposed approach combines event-driven monitoring, predictive exception identification, multi-agent reasoning, and coordinated decision execution to move manufacturing operations from reactive exception handling toward anticipatory intervention. Because the supplied reference set is predominantly concerned with MEMS capacitive pressure sensors rather than enterprise manufacturing systems, the literature review critically examines the transferable methodological principles of sensor modeling, sensitivity analysis, simulation, data-driven design, and comparative performance evaluation. These principles provide a theoretical foundation for designing reliable exception-detection mechanisms in digitally integrated manufacturing environments. The paper proposes an architecture in which manufacturing events are continuously evaluated, predicted deviations are classified according to operational impact, and specialized AI agents coordinate responses across production, inventory, procurement, maintenance, and logistics functions. The analysis indicates that predictive exception management can improve decision timeliness, reduce propagation of localized disruptions, and strengthen cross-functional coordination. However, model reliability, data quality, explainability, integration complexity, and autonomous decision risk remain significant limitations. The paper therefore positions AI-based exception management as a controlled decision-support and orchestration capability rather than an unrestricted autonomous replacement for manufacturing governance.