Hypercare Signal Fusion for Early Instability Detection in SAP S/4HANA and EWM Go-Lives
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Abstract
The hypercare phase following SAP go-live frequently determines the long-term success of enterprise transformation programs. Operational instability often emerges through weak signals distributed across monitoring systems, incident repositories, business transactions, and user feedback channels. This paper proposes Hypercare Signal Fusion, an AI-enabled framework for integrating heterogeneous operational signals to detect post-deployment instability before business disruption occurs.
The framework combines infrastructure telemetry, application logs, SAP monitoring data, incident tickets, business transaction failures, and user sentiment into a unified predictive intelligence model. Machine learning algorithms identify correlated anomalies while temporal analytics estimate the probability of escalating operational failures. Decision support dashboards prioritize remediation activities according to predicted business impact.
Evaluation demonstrates earlier identification of systemic instability, reduced incident resolution times, improved operational resilience, and more efficient allocation of hypercare support resources
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