A Signal-to-Action Methodology for Reducing Decision Latency in Fragmented Enterprise Operational Systems

Enterprise analytics programs frequently detect operational anomalies faster than organizations can convert them into governed action. This paper defines decision latency as the elapsed time between analytical signal detection and verified operational response, then proposes a Signal-to-Action methodology for fragmented enterprise environments. The method integrates six phases: business signal definition, data source mapping, decision ownership mapping, business rule translation, workflow activation, and traceability-based learning. The proposed architecture connects event ingestion, Physical Data Element (PDE) governance, signal scoring, rule evaluation, ownership resolution, workflow orchestration, and outcome feedback into a controlled decision loop. A discrete-event simulation was conducted over 180 operating days using 12 heterogeneous source systems, 250 supplier entities, 30,000 daily order-line updates, and four baselines: dashboard-only analytics, static alerting, centralized analytics queueing, and process-mining diagnosis. Results show that the proposed method reduced median decision latency from 18.6 h to 6.8 h relative to dashboard-only analytics, reduced P95 latency from 73.4 h to 25.9 h, increased exception throughput by 41.7%, improved action-closure rate from 61.5% to 88.6%, and reduced missed severe exceptions from 8.7% to 3.2%. The economic model produced USD 3.84 million in annualized net economic value and an ROI of 2.6. The findings position decision latency as a measurable enterprise architecture design variable rather than a residual management artifact.