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International Journal of Next-Generation Engineering and Technology

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Event-Driven Intelligent Manufacturing: Autonomous Exception Resolution Using Multi-Agent Generative AI and SAP S/4HANA

DOI https://doi.org/10.55640/ijnget-v03i09-01
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Kwame Mensah 1 , Ama Owusu 1 ,
4 Generative AI Research Unit, Accra Intelligent Technologies, Ghana
4 AI Systems and Automation Division, Ghana Digital Research Center, Ghana

Abstract

Modern manufacturing systems increasingly depend on rapid detection and resolution of process deviations involving equipment condition, grinding performance, tool setting, process compensation, energy consumption, and production quality. Conventional enterprise resource planning systems can record such deviations, but effective resolution requires the integration of operational intelligence with enterprise decision processes. This research develops a conceptual event-driven intelligent manufacturing framework that combines multi-agent generative artificial intelligence (AI) with SAP S/4HANA to support autonomous exception resolution. The framework interprets manufacturing events, identifies the probable operational cause, coordinates specialized AI agents, evaluates corrective alternatives, and initiates controlled enterprise actions. The theoretical foundation is derived from the supplied literature on grinding-process modeling, dynamic verification, wheel life, tool deflection, dressing performance, compensation modeling, grinding ratio, and specific energy. These studies collectively demonstrate that manufacturing deviations are measurable, dynamic, and interconnected rather than isolated events. The proposed architecture therefore treats operational exceptions as event streams requiring contextual reasoning rather than simple threshold alarms. The study conceptually evaluates the framework according to detection accuracy, causal interpretation, response latency, decision consistency, process traceability, and human-governed execution. Findings indicate that multi-agent orchestration can provide a stronger mechanism for connecting process-level evidence with enterprise-level corrective actions than isolated monitoring or rule-based intervention. However, autonomous execution introduces important limitations involving data quality, model uncertainty, conflicting agent recommendations, authorization, explainability, and operational risk. The proposed framework establishes a research foundation for integrating intelligent manufacturing analytics with SAP S/4HANA-oriented enterprise execution while maintaining controlled autonomy.

Keywords

Multi-Agent Generative AI, SAP S/4HANA, Event-Driven Manufacturing, Autonomous Exception Resolution

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