Agentic AI the Smart Sourcing Revolution for Order Management to Avoid Markdowns

Abstract

Retailers that fulfill online orders from stores usually source each order with static rules such as “ship from the nearest store” or “ship from the store with the most stock.” These rules ignore where a unit is most likely to sell at full price, so stock trapped in slow locations is eventually marked down while fast-selling locations run out. This paper presents an agentic order-sourcing architecture in which a Sourcing Orchestrator Agent, triggered by the order management system (OMS), collects evidence from five specialist sub-agents (pending stock, fulfillment reliability, local events, weather, and supply-chain continuity) and ranks candidate fulfillment nodes with a Fulfillment Priority Score (FPS) that prices the opportunity value of every unit on hand. We evaluate the approach in a seeded, fully disclosed simulation of a 12-store network over a 12-week season, using 300 paired replications against nearest-store, most-stock and weeks-of-supply baselines. FPS routing reduces units cleared at markdown from 24.9% to 19.5%, cuts missed walk-in sales by 34%, and raises gross margin by 4.9% (+$2,529 ± $95 per replication). Stock-balancing rules capture most of the markdown reduction, but they do so at about 1.5 times the shipping cost of FPS and roughly one extra day of delivery time. The gains grow with the online share of demand and stay positive under large forecast error. Because the evidence is simulated rather than field-measured, we treat the results as a controlled comparison of decision logic and outline a pilot design for validating them in production.