BUSINESS PROCESS MONITORING INTELLIGENCE IN SAP CLOUD ALM: A FRAMEWORK FOR DRIFT DETECTION, PREDICTIVE ANALYTICS, AND EXPLAINABLE INSIGHTS

Process owners in SAP-managed enterprises face a recurring operational gap: threshold-based alerting indicates that a KPI has crossed a boundary but provides no explanation of whether that movement reflects a gradual trend, expected seasonal variation, or a correlated pattern emerging across several related KPIs simultaneously. Acting on a single threshold breach without this context leads to delayed response, misrouted escalations, and missed early-warning signals that never individually crossed a boundary yet collectively represent a developing process risk. This paper proposes a Business Process Monitoring Intelligence framework that extends SAP Cloud ALM BPMon with category-aware drift detection and LLM-assisted predictive explanation. The framework maps standard and custom KPIs into process hierarchy nodes, validates data readiness and retention conditions, extracts KPI status and history through the Business Process Monitoring Analytics API, and classifies drift through four patterns: threshold breach, trend slope, seasonal deviation, and multi-KPI co-drift. A bounded LLM-assisted explanation layer then produces a process-owner-readable narrative, a short-horizon forecast with explicit invalidating conditions, evidence references, and a recommended action class routed through existing access group ownership. The framework draws on current SAP documentation for BPMon standard content, custom KPI instrumentation for both ABAP and BTP, custom process structures, data readiness prerequisites, and the BPMon Analytics API. No empirical results against live BPMon KPI data are reported; all drift detection and forecasting claims are design propositions for future empirical validation.