Adaptive SAP Supply Chains: AI/ML-Driven Anomaly Detection, Dynamic Network Reconfiguration, and Zero-Downtime BMS Upgrades with Real-Time Location Intelligence
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706006Keywords:
Adaptive supply chains, SAP, Artificial Intelligence, Machine Learning, Anomaly detection, Dynamic network reconfiguration, Zero-downtime upgrades, Building Management System (BMS), Real-time location intelligence, Digital twins, Predictive analytics, IoT integration, Supply chain resilience, SustainabilityAbstract
The evolution of adaptive supply chains within SAP ecosystems is being accelerated by the integration of Artificial Intelligence (AI), Machine Learning (ML), and real-time analytics. This paper presents a comprehensive framework for AI/ML-driven anomaly detection, dynamic network reconfiguration, and zero-downtime Building Management System (BMS) upgrades enabled by real-time location intelligence. The proposed system leverages predictive analytics, edge computing, and geospatial data fusion to continuously monitor operational parameters, identify deviations, and automatically reconfigure logistics and production networks in response to disruptions. Through intelligent orchestration of SAP modules and digital twin models, enterprises can achieve seamless system upgrades and resilient supply chain continuity without operational interruptions. The study demonstrates how the fusion of AI-driven predictive maintenance, dynamic routing algorithms, and real-time IoT sensor data can minimize downtime, enhance decision accuracy, and improve energy efficiency. Experimental results indicate that adaptive SAP supply chains using this integrated approach can achieve up to 40% faster anomaly resolution and a 25% reduction in system latency during upgrades. The research underscores the transformative potential of AI/ML in driving intelligent, self-healing, and sustainable supply chain ecosystems.
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