Amazon: Moving the Warehouse Closer
How regionalization turned fulfillment geography into faster delivery, fewer miles, and lower cost
Brand Learning · Location Changed Everything · Case 14 · Collection I · Prepared October 2026
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Amazon’s regionalization shows that facility proximity and inventory proximity are different problems. A nearby building cannot deliver quickly if the item the customer wants is stored across the country.
The location decision
Amazon’s fulfillment network was built for selection at enormous scale. As volume and assortment expanded, a nationally optimized system created too many long moves and intermediate touches. Packages could cross the country even when a fulfillment building existed near the customer because the correct inventory had not been positioned there.
The company and its operations-research team redesigned the United States network around eight largely self-sufficient regions. Inventory placement models predicted which products each region would need; routing rules sought to keep orders inside the region when doing so preserved selection and service. The change was deployed in March 2023 after extensive testing.
The published results were material: a 15 percent reduction in distance traveled, 12 percent fewer middle-mile touchpoints, an increase in orders fulfilled entirely within-region from 62 percent to 76 percent, and savings greater than $0.45 per unit. The case won the 2024 INFORMS Prize for applied analytics. Those numbers are especially instructive because they measure network consequences, not the prestige of any single warehouse address.
Regionalization did not replace local facilities. Amazon says its network includes more than 200 fulfillment centers and uses forecasts to position hundreds of millions of products. Smaller same-day facilities place high-demand inventory near metropolitan customers and combine functions that once occurred in separate buildings. The hierarchy—national, regional, metropolitan, last-mile—allows different nodes to solve different distance problems.
The strategic model
Inventory geography
The right item must be near the likely order before the customer clicks.
Regional self-sufficiency
Regions reduce cross-country movement without requiring every facility to stock everything.
Node specialization
Large fulfillment centers, sort centers, same-day sites, and delivery stations solve different stages of the trip.
Network measurement
Distance, touches, cost per unit, speed, and in-region fulfillment expose the effect of each facility decision.
What the strategy got right
It redesigned the network around the customer promise rather than preserving a legacy flow.
It combined facility placement with inventory forecasting.
It used controlled testing and measurable network outcomes.
It maintained multiple facility types instead of expecting one building format to do every job.
Where the model can fail
Forecast error can place inventory close to the wrong demand, increasing transfers, markdowns, or stockouts.
Warehouses create land, labor, transportation, tax, and community impacts that network savings do not erase.
Regionalization can sacrifice some pooling efficiency and requires rules for exceptions and low-frequency products.
Large fixed networks are exposed when demand patterns, service promises, or transportation economics change.
The impact
Amazon’s case changes the meaning of a “good warehouse location.” The answer depends on the inventory assigned to it, the nodes around it, the orders it can serve, and the handoffs it removes. Location performance belongs to the network, not the building alone.
Questions for leaders and franchise consultants
Which customer promise is each facility type responsible for?
What proportion of demand can be served within the proposed region?
Which inventory should be placed locally, regionally, or nationally?
How many miles and handling steps does the node remove?
How will the network respond when forecasts or local operations fail?
Applying the lesson with Wanderland
Wanderland can support the geographic layer of network planning by mapping demand, facilities, drive-time and road relationships, labor markets, business ecosystems, territories, and candidate regions. It should complement—not replace—inventory optimization, capacity simulation, cost modeling, and site engineering.
Research sources
Independent educational analysis. Company names identify the subjects of the stories; no endorsement or affiliation is implied. Historical results retain their stated source periods.
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