An Intelligent Multi-Agent Architecture for Predictive Exception Management in SAP S/4HANA Manufacturing and Supply Chains
DOI
Keywords
Abstract
Modern manufacturing and supply-chain environments increasingly depend on integrated enterprise systems capable of coordinating production, procurement, inventory, maintenance, quality, and logistics decisions. SAP S/4HANA provides a transactionally integrated environment, but the growing volume and velocity of operational events creates a significant exception-management challenge: conventional rule-based alerts frequently identify deviations only after operational thresholds have been exceeded. This research proposes an intelligent multi-agent architecture for predictive exception management in SAP S/4HANA manufacturing and supply chains. The architecture conceptualizes specialized software agents responsible for event monitoring, anomaly detection, production analysis, maintenance reasoning, procurement coordination, supply-chain impact assessment, and decision orchestration. The theoretical foundation is derived from the provided literature on fault simulation, diagnosis, health assessment, hydraulic-system modeling, structural optimization, and condition-oriented engineering analysis. These studies collectively demonstrate the value of modeling system behavior, detecting deviations, assessing risk, and linking technical conditions to corrective decisions. The proposed architecture extends these principles into an enterprise-management context by transforming isolated exception signals into coordinated, predictive decisions. The framework emphasizes event-driven processing, agent specialization, shared operational context, confidence-aware reasoning, escalation mechanisms, and human supervision. The analysis indicates that multi-agent coordination can improve exception prioritization, reduce fragmented decision-making, and establish a more adaptive relationship between operational events and SAP-based business processes. However, challenges remain concerning data quality, agent coordination, explainability, false-positive control, integration complexity, and human accountability. The study therefore positions multi-agent intelligence not as a replacement for SAP transactional governance, but as an adaptive decision layer operating above integrated enterprise processes.
References
Ding X, Zhou J, Shi Z, et al. Fault simulation and experimental verification of axial piston pump based on AMEsim. 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing). 2021.p.1-6.
Duan S, Li Y, Cao Y, et al. Health assessment of landing gear retraction/extension hydraulic system based on improved risk coefficient and FCE model. Applied Sciences 2022; 12(11): 5409.
Guo WG, Han W. Modeling and faults simulation of aircraft landing gear system. Applied Mechanics and Materials. 2013, 333: 2021-2024.
Ma Q, Song D, Liu B. Fault mode analysis and simulation verification of hydraulic system based on AMEsim. Journal of Physics: Conference Series 2021; 2006(1): 012013.
Maré JC, Fu J. Review on signal-by-wire and power-by-wire actuation for more electric aircraft. Chinese Journal of Aeronautics 2017; 30(3): 857-870.
Pappalardo CM, Manca AG, Guida D. A combined use of the multibody system approach and the finite element analysis for the structural redesign and the topology optimization of the latching component of an aircraft hatch door. IAENG International Journal of Applied Mathematics 2021; 51(1):175-191.
Schmiedel P, Holzheimer M. Development of CFRP aircraft doors with the interaction of NDT and strength analysis. 19th World Conference on Nondestructive Testing. 2016.
Tang J, Liu Q, Hu J, et al. Leakage fault diagnosis method of aircraft landing gear hydraulic cylinder based on wavelet packet. The Journal of Engineering 2019; (13): 427-431.
Wang X, Cai Y, Geng X. Integrated optimization of composite bay door with pre-deformation and variable thickness design. Struct Multidisc Optim 2019;59: 1673-1684.
Zhang C, Liu R, Tao C, et al. Design, construction, and modeling of aircraft door sealing plate based on SMAs. International Journal of Smart and Nano Materials 2022; 13(3): 481-503.
Zhou J, Yu L, Sun K, et al. Modeling and fault diagnosis of aviation hydraulic servo actuator. The Proceedings of the 2021 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2021). 2022.p.253-268.
Zhu S, Ge Z, Ma T, et al. Modeling and fault simulation of aircraft lock mechanism. 2022 Prognostics and Health Management Conference (PHM-2022 London). 2022.p.228-231.
M. Kalal, "Predictive Production Planning in Advanced Manufacturing Using SAP PP and Analytics," 2026 5th International Conference on Sentiment Analysis and Deep Learning (ICSADL), Birendranagar, Nepal, 2026, pp. 1363-1370, doi: 10.1109/ICSADL67539.2026.11452022.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Chinedu Okafor, Amina Ibrahim
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their articles published in this journal. All articles are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited.