Cost-to-Serve Analytics for Fragmented Supply Chains: A Cross-System Decision Support Framework

Cost-to-serve analysis is a critical finance and operations capability because profitability across customers, products, channels, suppliers, and logistics is often distorted by aggregated cost allocation. In fragmented supply chains, cost drivers such as expedited freight, low-volume orders, manual rework, supplier instability, inventory holding costs, service exceptions, and long-tail SKU complexity are distributed across ERP, procurement, warehouse, transportation, customer service, planning, and finance systems. This paper proposes a Cost-to-Serve Signal Framework for estimating and acting on service-cost drivers in fragmented enterprise environments. The methodology consists of six stages: cost-driver signal identification, cross-system cost-pathway mapping, activity and driver classification, cost-to-serve exposure translation, ownership and intervention routing, and profitability outcome measurement. The reference architecture preserves legacy systems of record while adding a governed analytics control layer for Physical Data Element (PDE) mapping, cost-driver signal extraction, activity-based allocation, service-exposure scoring, decision-rule orchestration, workflow activation, and outcome traceability. A discrete-event simulation was conducted over 180 operating days using 14 enterprise systems, 1.4 million order lines, 520,000 warehouse events, 280,000 transportation events, 410,000 purchase-order lines, and 48,000 service exceptions. Results show that the proposed framework reduced median cost-to-serve intervention latency from 58.2 h to 13.7 h relative to spreadsheet-based cost analysis, improved customer-product profitability classification accuracy from 61.5% to 87.6%, reduced manual cost reconciliation effort by 66.9%, improved high-cost exception closure from 38.4% to 81.2%, and generated USD 6.94 million in annualized net economic value. The findings position cost-to-serve analytics as a governed cross-system decision-support discipline rather than a retrospective costing exercise.