Advanced Computing Frameworks for Real-Time SAP S/4HANA Retail Business Intelligence: Optimizing Data Processing, Latency, and System Reliability

Abstract

This study addresses the persistent problem that SAP S/4HANA retail business intelligence environments often struggle to achieve simultaneously low latency, high data freshness, and dependable reliability at enterprise scale, particularly when cloud and hybrid deployments introduce additional integration and operational complexity. The purpose was to quantify which advanced computing framework combinations are most consistently associated with improved real-time BI outcomes in retail decision workflows. Using a quantitative cross-sectional, case-based design, the study coded a sample of 60 eligible enterprise and cloud-oriented cases from the reviewed corpus (N = 60). Key variables included framework adoption categories (for example CDC or incremental refresh, in-memory or HTAP, streaming, distributed processing, cloud-native orchestration), outcome constructs measured via a 5-point Likert evidence scale (Latency Improvement Evidence LIE, Processing Efficiency Evidence PEE, Reliability and Continuity Evidence RCE), and workflow clusters (inventory, promotions, fulfillment, anomaly detection). The analysis plan applied frequency distributions and descriptive statistics, then conducted evidence-based hypothesis aggregation using a predefined support rule (at least 60% directional support and mean evidence score at or above 3.50). Findings show strong concentration of CDC or incremental refresh integration at 73.3% (44/60), in-memory or HTAP execution at 68.3% (41/60), streaming at 60.0% (36/60), cloud-native orchestration at 51.7% (31/60), and distributed processing at 48.3% (29/60). Overall evidence strength was high for latency and processing (LIE M = 3.84, SD = 0.71; PEE M = 3.76, SD = 0.69) with moderate-strong reliability (RCE M = 3.62, SD = 0.74). Headline results supported all three hypotheses: hybrid multi-layer stacks outperformed single-layer designs for latency (77.8% supportive, 28/36; hybrid LIE M = 4.08 vs 3.41), CDC and event-driven strategies outperformed batch refresh for freshness and reporting delay (CDC subgroup LIE M = 3.97 vs 3.22), and observability plus automated failover improved reliability versus monitoring-only patterns (high RCE 74.1% vs 42.1%; RCE M = 3.98 vs 3.28). Implications suggest practitioners should prioritize CDC-driven ingestion and incremental maintenance, adopt hybrid stacks that combine in-memory, streaming, and distributed compute, and operationalize reliability through observability and automated recovery, while tracking end-to-end latency as a decomposed pipeline metric aligned to core retail workflows where inventory (30.0%) and promotion monitoring (26.7%) dominate use cases.