Born at the Dock argues that a supply chain plan is not complete when software approves it; it is complete only when the physical network can execute it and the customer receives the order in full. The whitepaper reframes planning around the dock, where inventory must be found, cases picked, pallets built, shipments staged, trailers loaded legally, carriers dispatched, and destinations prepared to receive. It introduces the “Dock Test,” five questions that every replenishment decision should answer before commitment: Can the origin execute it? Can transportation move it? Can the load be built? Can the destination absorb it? Can the process be repeated?
Drawing on Tom Moore’s operating experience and the warehouse problem that led to ProvisionAi’s original load-building work, the paper explains the execution gap between what APS, ERP, TMS, and WMS systems approve and what sites, carriers, trailers, and receiving operations can actually absorb. It shows how daily shipment volatility, assumed capacity, conservative loading practices, and manual workarounds create planning debt in the form of detention, overtime, rejected tenders, reloads, short shipments, and unused legal payload.
The paper then presents two connected decisions. LevelLoad shapes network flow across a rolling horizon so freight is released in a capacity-constrained, service-aware cadence. AutoO2 converts each order into a legal, stable, damage-aware, and repeatable load using interacting rules for weight, cube, axle distribution, stackability, sequencing, and handling. Operational examples from Kimberly-Clark and Riviana Foods demonstrate how executable planning can reduce variability, improve tender acceptance, lower freight cost, shorten training, and move the same volume in fewer, better-built trucks.
Drawing on Tom Moore’s operating experience and the warehouse problem that led to ProvisionAi’s original load-building work, the paper explains the execution gap between what APS, ERP, TMS, and WMS systems approve and what sites, carriers, trailers, and receiving operations can actually absorb. It shows how daily shipment volatility, assumed capacity, conservative loading practices, and manual workarounds create planning debt in the form of detention, overtime, rejected tenders, reloads, short shipments, and unused legal payload.
The paper then presents two connected decisions. LevelLoad shapes network flow across a rolling horizon so freight is released in a capacity-constrained, service-aware cadence. AutoO2 converts each order into a legal, stable, damage-aware, and repeatable load using interacting rules for weight, cube, axle distribution, stackability, sequencing, and handling. Operational examples from Kimberly-Clark and Riviana Foods demonstrate how executable planning can reduce variability, improve tender acceptance, lower freight cost, shorten training, and move the same volume in fewer, better-built trucks.