Study reduces cash conversion delays in fragmented enterprise systems, suggesting improved financial efficiency.
Corporate finance functions frequently possess sufficient reporting but insufficient execution speed. Financially relevant operational signals are often distributed across ERP, sales, procurement, inventory, logistics, treasury, customer-service, and reporting systems, creating delays between signal detection and corrective finance-led action. This paper introduces financial decision latency as the elapsed time between a financially material business signal and a verified corrective action affecting cash, margin, revenue risk, or working capital. A Finance Signal-to-Action Framework is proposed with six stages: financial signal identification, cross-system data mapping, financial impact translation, ownership and escalation mapping, workflow activation, and outcome measurement. The architecture preserves existing systems of record while adding a governed analytical control layer for Physical Data Element (PDE) mapping, signal extraction, cash-impact scoring, decision-rule orchestration, and traceable action closure. A discrete-event simulation was conducted over 180 operating days using 11 fragmented enterprise systems, 180,000 open receivable items, 95,000 inventory records, 42,000 purchase-order lines, and five baseline architectures: spreadsheet reconciliation, dashboard-only finance reporting, static ERP alerts, centralized finance analytics queueing, and process-mining diagnosis. Results show that the proposed framework reduced median financial decision latency from 42.8 h to 9.4 h relative to spreadsheet reconciliation, reduced P95 latency from 126.5 h to 34.7 h, lowered blocked-invoice resolution time by 61.8%, reduced preventable DSO drift by 28.6%, improved cash-risk exception closure from 54.2% to 89.1%, and produced USD 5.26 million in annualized net economic value. The findings position finance decision latency as a measurable control variable in enterprise architecture and working-capital management.
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Donepudi et al. (2024) studied this question.
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