Working-capital performance depends on the speed with which enterprises convert operational signals into cash-impacting action. In legacy ERP environments, order, inventory, billing, collections, logistics, customer, and finance data are frequently fragmented across multiple systems of record. This fragmentation delays recognition of order holds, shipment delays, billing blocks, dispute codes, aging inventory, pricing exceptions, and payment-risk signals. This paper proposes a Signal-to-Cash Methodology for working-capital analytics in fragmented enterprise environments. The methodology consists of six stages: cash-impact signal detection, order-to-cash pathway mapping, exception classification, action ownership, resolution prioritization, and cash-conversion measurement. The proposed architecture preserves legacy systems of record while adding a governed analytics control layer for Physical Data Element (PDE) mapping, signal extraction, financial-exposure scoring, decision-rule orchestration, workflow activation, and outcome measurement. A discrete-event simulation was conducted over 180 operating days using 12 legacy ERP and operational systems, 220,000 order lines, 160,000 open receivable items, 105,000 inventory records, and 26,000 dispute cases. Results show that the proposed method reduced median signal-to-cash latency from 46.8 h to 10.9 h relative to spreadsheet reconciliation, reduced order-to-cash cycle time by 7.1 days, reduced preventable DSO drift by 34.2%, improved cash-impact exception closure from 52.4% to 88.7%, and generated USD 6.18 million in annualized net economic value. The findings position working-capital analytics as a governed signal-to-action discipline rather than a retrospective finance reporting capability.
Thatikonda et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: