Infrastructure for Network Design & Lane Optimization

Network design requires documented, findable business constraints: customer service level requirements that define maximum transit days by lane, facility cost structures (lease terms, labor costs, throughput capacity), and strategic rules about which markets must be served from owned versus third-party facilities. These constraints bound the optimization space. The freight baseline confirms this knowledge is tribal — a senior VP knows why the Memphis facility exists but it's not documented. Without findable business constraint documentation, the AI recommends network configurations that violate undocumented strategic commitments.

Network optimization requires systematic capture of multi-year shipment history — origins, destinations, volumes, lane costs, and service performance — through defined TMS workflows. The baseline confirms TMS captures load details and EDI creates systematic data flows. For network design, the capture requirement extends to facility cost data (captured through finance systems) and demand forecasts (captured from sales/ERP). Template-driven capture ensuring consistent multi-year lane volume and cost records is the minimum for identifying consolidation opportunities and hub location analysis.

Network design optimization requires formal ontology connecting shipment lane records to facility cost structures, customer service requirements, and demand forecasts. The model must compute: for each origin-destination pair, what is the cost-service impact of routing through facility X versus Y versus eliminating the lane? This requires explicit relationship mapping between Lane entities and Facility entities, with cost and service attributes attached. L4 formal structure enables the optimization engine to evaluate network configurations across thousands of lane-facility combinations without manual data assembly for each scenario.

Network design optimization requires API access to multi-year TMS shipment history, facility cost databases, customer service requirement repositories, and demand forecast systems. These systems must be queryable by the optimization engine for scenario modeling. The freight baseline confirms legacy TMS limits API access, but connecting to historical shipment data and facility cost systems via API is necessary for the model to generate network scenarios programmatically rather than requiring analysts to manually assemble multi-system datasets for each analysis.

Network design is a strategic planning capability that operates on multi-year patterns rather than real-time operational data. The underlying network configuration recommendations change on an annual or multi-year cycle, not in response to daily operational events. Scheduled periodic review of lane volume data, facility cost inputs, and demand forecasts — aligned with annual planning cycles — is appropriate for this capability. Unlike ETA prediction or carrier acceptance models, network design recommendations don't require event-triggered or near-real-time data maintenance.

Network design optimization can function with point-to-point integrations: TMS historical lane data connects to the optimization engine, finance systems provide facility costs, and ERP provides demand forecasts. Full API-based integration across all freight systems isn't required because network design is a periodic strategic analysis rather than a continuous operational workflow. Targeted integrations to extract the multi-year datasets needed for scenario modeling are sufficient for this capability's stabilisation needs.