Key factors include location relative to ports/airports, carrier pickup frequency, real estate and labor costs, available value-add services (kitting, returns), WMS/API maturity, SLA terms, and slotting/throughput capabilities. Prioritize the metrics that impact your lead time and cost-to-serve.

Closer proximity reduces drayage time and variability, enabling tighter cutoffs and more carrier options. In practice this can lower transit days, reduce demurrage risk, and allow selection of higher-frequency feeders and airfreight carriers to match service-level targets.

Require a modern WMS with real-time inventory, APIs for order and shipping integration, scalable racking and picking configurations, kitting/assembly lanes, cross-dock capacity, and established returns processing. These capabilities reduce order cycle time and enable multi-channel fulfillment strategies.

Compare current baseline (freight, warehousing, handling, returns) to modeled scenarios in Au Cap, using carrier lead-time matrices, zone-rate models, and inventory-carrying cost assumptions. Run sensitivity tests for volume, SKU velocity, and peak seasons to quantify cost-to-serve and service improvements.

Negotiate clear SLAs for order accuracy, OTIF, pick-to-ship lead time, inventory accuracy, and claims resolution. Standardize reporting cadence and format, and include penalty/bonus clauses tied to measured KPIs to align performance and accountability.