When a jacket sits unsold on a store rack the instinct is often the same: mark it down and clear the floor. That response can be expensive if that exact unit is routinely fulfilling online orders for customers in another market. Treating in-store, warehouse and online stock as separate silos encourages premature discounts, blunt allocation and avoidable margin loss.

Retailers that treat all physical units as one networked asset change the rules for pricing and allocation. A unified inventory view combines sales and fulfillment telemetry so planners can decide where to hold price, where to promote earlier, and which stores should act as fulfillment hubs. The difference is practical: fewer knee-jerk markdowns, smarter transfers, and pricing tied to how customers actually buy and receive goods.

Store inventory as a fulfillment “release valve”

For decades, stock placed in a store largely stayed there; transfer costs made redistribution rare. Omnichannel fulfillment—ship-from-store, buy-online-pickup-in-store, and similar flows—changed that math. Store units now play two roles: local retail and networked fulfillment. That dual function means in-store sell-through alone can be a misleading performance signal.

Nicholas Wegman, Ph.D., senior director and artificial intelligence scientist at Zebra Technologies, describes store inventory as a “release valve” for digital demand: items that look slow at one location may be supporting orders elsewhere. Visibility into those fulfillment flows helps retailers avoid markdowns driven by incomplete data and better decide where to hold safety stock or move product.

Rethinking markdowns: calendar schedules vs. demand signals

Many retailers still follow calendar-based markdown schedules: a product slides through predefined discount steps as a season progresses. That simplifies operations but treats price as a timetable instead of a lever tied to demand. When calendar rules dominate, discounts become a blunt instrument instead of a targeted remedy.

A unified inventory approach replaces calendar-only rules with signal-driven triggers. Combining fulfillment and sales data lets planners see whether an item has a realistic path to target sell-through without discounting. If such a path exists, hold price. If network signals show no feasible route to sell-through before a critical date, trigger earlier promotion to avoid steeper markdowns later in the season.

Two capabilities matter: reliably measuring fulfillment flows (for example, how often online orders are filled from stores or regional hubs) and setting realistic network-wide sell-through goals. With those inputs, pricing can vary by location and channel instead of being applied uniformly across the chain.

Allocation matters: stop “peanut-butter” decisions

Allocation sets the starting condition for pricing. Traditional allocation often uses broad rules—minimum quantities, full-size runs or similar assortments across many stores—because manual processes lack the bandwidth for finer-grained decisions. The result is inventory imbalances that create markdown pressure later.

AI-driven allocation models account for local demand nuances and the operational role of each location. Some stores act as fast local fulfillment points; others are regional hubs optimized for ship-from-store volume. Allocation that considers those roles reduces overstocks in low-demand stores and understocks at high-fulfillment locations, cutting the need for reactive price cuts and costly transfers.

Key inputs for smarter allocation include historical purchase behavior by store, online fulfillment routing logic, seasonal patterns and SKU-level performance across channels. When these inputs feed an optimization engine, retailers can shorten delivery times, reduce transfer costs and lower markdown incidence.

Practical first steps

Moving toward a single inventory pool is an operational program, not just a software purchase. Begin with focused, measurable changes:

  • Capture fulfillment metrics: track how often online orders draw from each store and how routing rules affect inventory flows.
  • Unify inventory records: consolidate POS, warehouse management and e-commerce stock data so planners see network availability in near real time.
  • Define store roles: designate fulfillment hubs and locally focused stores, and use those roles in allocation logic.
  • Use sell-through goals, not just calendar dates: let pricing rules react to realistic network-wide paths toward targets.
  • Pilot before scale: test the approach on select categories or regions to validate fulfillment elasticity and markdown impact.

Vendors are packaging lifecycle pricing and allocation tools that combine these capabilities; implementations vary in integration complexity, data quality requirements and change management needs. Those operational constraints determine how quickly a retailer will see benefits.

What to watch next

Retailers should measure whether pilots produce clear, metric-driven improvements: reductions in seasonal markdown depth, lower transfer costs, and a higher share of online orders fulfilled from optimized stores. Those outcomes—not vendor claims—will prove whether a single inventory pool is delivering margin and service improvements in practice.