Evaluate throughput capacity, peak-season scalability, and real dock/yard capacity first. Require WMS and TMS APIs for order flows, inventory visibility, and EDI/CSV options. Define KPIs—pick/pack rate, order accuracy, dock-to-ship lead time—and include SLA penalties and ramp-up timelines to align incentives and predict costs.

Prioritize proximity to national highways and rail terminals used by regional carriers, frequency of LTL services, and availability of TL lanes to key destinations. Review terminal handling times, consolidation windows, and multimodal options that reduce linehaul costs and inventory-in-transit days for bulk B2B flows.

Insist on SKU-level inventory accuracy, return-to-stock cycle time, kitting throughput, and same-day pick/pack cutoffs. Confirm capacity for slotting optimization, pick-path efficiency, and integrated reverse-logistics workflows to preserve service levels across web, marketplace, and retail channels.

Lower rent and labor reduce per-order storage and handling costs, improving margins on lower-price SKUs. Model trade-offs including slightly longer lead times to some metros, potential limits on local carrier frequency, and incremental inventory days. Run lane-level cost models to quantify net landed savings.

Start with a pilot SKU set and agreed throughput, validate WMS integrations and SLAs, then scale slotting and capacity in defined phases tied to volume triggers. Include clear forecasting windows, contingency capacity clauses for peaks, and a playbook for rapid SKU onboarding to avoid service disruption during growth.