Demand Sensing at the Fulfillment Edge: Algorithmic Retail Physics

A global athletic apparel and footwear brand engaged Taller to embed backend and data-engineering capacity in its North America supply-chain and fulfillment operations.

Matching inventory to demand was what decided whether digital growth was actually profitable. Mismatched inventory meant split shipments, markdown pricing, and costly returns; matched inventory meant more full-price sales and a lower cost to serve. The old retail model treated inventory as a fixed plan set in advance, so the real challenge for this client was to turn fulfillment into a continuous, real-time decision: weighing size, color, demand, channel, margin, delivery promise, return risk, warehouse capacity, and labor for every single unit, at the moment of order.

The client’s fulfillment model was digital-first. A third-party predictive-analytics platform sensed demand; inventory was positioned in advance at regional service centers on both US coasts; ship-from-store and buy-online-pickup-in-store turned physical stores into fulfillment points; and warehouse robotics (thousands of robots across the distribution centers, including the flagship facility) automated the heavy lifting. Taller’s role sat with the North America Capacity team, a pod working inside the client’s broader supply-chain engineering function, with its backend and data-engineering work feeding the operational data the fulfillment layer ran on.