# Physical AI for supply chain and logistics: questions answered Canonical: https://codeatoms.ai/sectors/supply-chain-and-logistics/ License: CC BY 4.0 Publisher: CodeNinja Atoms (https://codeatoms.ai) ## What does a physical AI system for supply chain and logistics look like? A complete physical AI design for supply chain and logistics names what to sense, which existing systems to join, the object model that joins them, the models and hardware, the three-year cost and the person who approves every action. CodeNinja Atoms has published 3 such reference architectures for supply chain and logistics, each free to reuse under CC BY 4.0. Industrial Data Ontology for a Cold Chain From Plant to Store in the United States (United States): An open reference architecture for system design in physical AI, built as an industrial data ontology for a refrigerated and frozen food manufacturer in the United States that ships from its plants through its own and third party cold storage to retail distribution centers and stores, where owning the reasoning node that runs it costs about 591,000 US dollars over three years. When a retailer reports a soft case, or a regulator names a lot under the federal food traceability rule, the design joins the plant's lot, the warehouse's license plate, the carrier's reefer readings, the transport shipment line and the retailer's receipt into one journey, so the quality team can say within the hour which pallets shared a dock and a truck and produce the lot's whole record in hours instead of a week and three phone calls. The model runs entirely on the operator's own hardware inside the United States, with no third party model service. Sovereign AI Architecture for a Freight Corridor and Customs Clearance in Saudi Arabia (Saudi Arabia): An open reference architecture for system design in physical AI, built for a national freight and logistics operator in Saudi Arabia that moves containers and bulk from seaports to dry ports and freight yards by rail and road across corridors of more than two thousand kilometres, where owning the inference node that runs it costs about 569,000 US dollars over three years. When a declaration stalls at the seaport while the road leg runs late for its train, the design joins the declaration, the container, the train and its handover slot, the road leg and the open customer case into one consignment story, so a corridor planner sees the stall, the connection it will cost and the customer who will call, and approves the chase and the re booking before the call comes. All reasoning runs on the operator's own hardware in the country under the national data protection law, the customs authority's interface terms and export control on the GPU node. A Self-Improving AI System for Carrier Exception Handling in the United States (United States): An open reference architecture for system design in physical AI, built for a third party logistics provider in the United States that plans, tenders and tracks tens of thousands of truckload and less than truckload shipments a month for shippers across hundreds of contracted carriers, where owning the inference hardware that runs it costs about 606,000 US dollars over three years. When a truck breaks down on a multi stop load at night, the design joins the shipment and its stops, the live tracking, the appointment windows, the carrier's scorecard and contract, the claims record and the threads where the same exception was worked before into one argument, so the coordinator sees the cascade to the next stops, the carrier swap and the appointment changes drafted, and the shipper told before the first missed window. Every model runs on the operator's own hardware under the shippers' data terms, the carrier contracts, the federal hours of service rules and the applicable privacy law for driver data. ## Which AI models can a supply chain and logistics operator run on its own hardware? Each published supply chain and logistics design names its models and why, and every model is open-weight or no model is used at all, so the operator can run it on hardware it owns. Industrial Data Ontology for a Cold Chain From Plant to Store in the United States: GLM 5.3, named by the operator, as the frontier class open weight work surface for the mapping, excursion and trace agents and the cited conversational interface, under a license whose attribution notice and managed service trigger counsel reviews before hosting; document reading and embedding entries by class on an air cooled PCIe server; no entry survived live verification on this run, so the register holds catalog entries and the commitment waits on a verified row; nothing is trained or fine tuned, improvement runs through the memory of confirmed joins. Sovereign AI Architecture for a Freight Corridor and Customs Clearance in Saudi Arabia: GLM 5.3 (class open weights, self hosted, frontier class) named by the requirement for the command center that answers questions about a consignment with citations, with GLM 5.2 under MIT as the licence fallback; the register commits no model row until a live verification passes; no third party model service, nothing trained into weights. A Self-Improving AI System for Carrier Exception Handling in the United States: GLM 5.3 (GLM-5.3 bespoke license, 753B at FP8) for the triage, recovery and review drafting, the command center's cited conversational interface and ontology maintenance; Qwen3.8 27B (Apache-2.0, FP8) at the site tier parsing years of threads and notes; Qwen3-Embedding 0.6B (Apache-2.0) for retrieval over threads, notes and resolutions; no third party model service, nothing trained into weights. ## How much compute and hardware does AI in supply chain and logistics need? The compute follows from the models: the published supply chain and logistics designs size it as follows, from no new hardware to a full GPU node. Industrial Data Ontology for a Cold Chain From Plant to Store in the United States, compute: One central tier in the operator's existing server room in the United States: one node of eight 141 GB HBM cards (1,128 GB) drawing about 10 kW, holding 756 GB of FP8 weights with about 280 GB of KV cache headroom; no compute in freezers, on docks or in trucks, because readings arrive as partner data; inbound feeds only, no egress path, updates through the Harbor registry. Sovereign AI Architecture for a Freight Corridor and Customs Clearance in Saudi Arabia, compute: One node of eight 141 GB HBM cards in the operator's own in-country data center, holding about 756 GB of FP8 weights on about 1,038 GB of usable memory with about 280 GB of KV cache ceiling, planned at about ten kilowatts; no edge tier, no cameras, no new field sensing. The node is a 4A090 class item for a D:3 and D:4 destination, so an export licence determination against ECCN 3A090 and 4A090 is a phase zero checkpoint before any GPU class is quoted. A Self-Improving AI System for Carrier Exception Handling in the United States, compute: One node of eight 141 GB HBM cards (1,128 GB) in the operator's own facility, holding 753 GB of FP8 weights with the 1.2 planning factor, 904 GB, and about 280 GB of KV ceiling for concurrent coordinator sessions; one 48 GB PCIe card in an air cooled server for the parsing and embedding models; no edge tier and no cameras, the trucks are seen through the tracking feeds the operator already owns. ## Is it cheaper to own AI hardware or rent cloud GPUs in supply chain and logistics? Each supply chain and logistics design prices three years of ownership in its Appendix A, with every price cited, against renting the same capacity from a cloud region at its deepest three-year commitment where hardware is bought. Industrial Data Ontology for a Cold Chain From Plant to Store in the United States: three-year cost, About 591,000 US dollars to own the node for three years (a range of 546,000 to 615,000: node, support and power at the national average commercial or industrial price), against about 625,000 to rent the same eight cards on the deepest three year commitment in AWS US East (N. Virginia), so ownership costs about nine tenths of it, and about 1.66 million on demand, against which ownership costs about a third. Renting is a price reference only, because every rented option would carry product, lot and partner data out of the operator's hardware (Appendix A). Sovereign AI Architecture for a Freight Corridor and Customs Clearance in Saudi Arabia: three-year cost, About 569,000 US dollars to own the node for three years (a range of 541,000 to 594,000: server, support and power at the national commercial electricity tariff), against about 751,000 to rent the same eight cards on the deepest three year commitment in AWS Middle East (UAE), so ownership costs about three quarters of it, and about 2.00 million on demand, against which ownership costs a little over a quarter. Renting is a price reference only, because AWS listed no Saudi region on the day read and every rented option would carry cargo, customer and declaration data across the border (Appendix A). A Self-Improving AI System for Carrier Exception Handling in the United States: three-year cost, About 606,000 US dollars to own both tiers for three years (a range of 577,000 to 665,000: the node, the card, support and power at national average prices), of which the node is about 591,000, against about 644,000 to rent the same hardware on the deepest three year commitment in AWS US East (N. Virginia), so ownership costs about 94 percent of it, and about 1.71 million on demand, against which ownership costs about a third. Renting is a price reference only, because every rented option would carry shippers' freight data and the threads out of the operator's facility (Appendix A). ## What ontology or object model does a supply chain and logistics AI system need? The object model is the part that makes the system an ontology rather than a pipeline: typed objects for the things in the supply chain and logistics operation, with properties, status values and typed links. Every published design ships its object model as hyper-ontology/1 JSON that loads into Hyper Ontology. Industrial Data Ontology for a Cold Chain From Plant to Store in the United States: Product, Lot, Pallet, Case, Critical tracking event, Custodian, Location, Dock, Truck, Reefer reading, Excursion, Hold, Release, Complaint, Receipt, Traceability record. Sovereign AI Architecture for a Freight Corridor and Customs Clearance in Saudi Arabia: Consignment, Container, Declaration, Train, Wagon, Road leg, Haulier, Terminal, Dry port, Handover slot, Exception, Customer case, Corridor planner, Yard controller, Service desk agent. A Self-Improving AI System for Carrier Exception Handling in the United States: Shipper, Order, Shipment, Stop, Appointment, Carrier, Driver, Tracking event, Exception, Exception type, Action, Resolution, Standing fix, Claim, Scorecard entry, Exception coordinator. ## Who approves the decisions an AI system makes in supply chain and logistics? In every published supply chain and logistics design a named person makes the decision that changes the physical world; the system prepares it. Industrial Data Ontology for a Cold Chain From Plant to Store in the United States: Agents draft and people decide, by name: the traceability analyst confirms every identifier join the mapping agent drafts, the quality manager approves every hold the excursion agent recommends and acts in the quality hold and release system, the regulatory affairs lead sends every trace record the trace agent drafts; no agent places or lifts a hold or sends a record to a regulator, an escalation view surfaces unacknowledged excursions, and every confirmed join is kept as memory the next join reads. Sovereign AI Architecture for a Freight Corridor and Customs Clearance in Saudi Arabia: Agents draft and people decide, by name: the corridor planner approves the stall chase and the revised handover slot, the yard controller approves every re booking, and the service desk agent sends the cited answer; approval authority is a property of the actor objects, a per shift alert budget is agreed before thresholds are set, the action rate is itself a gate, and every confirmed stall cause and accepted re booking is kept as memory the next plan reads. A Self-Improving AI System for Carrier Exception Handling in the United States: Agents draft and coordinators decide, by name: the exception coordinator confirms the triage draft and approves the carrier message, the appointment change and the shipper notice, the operations manager approves each standing fix, the approver is recorded on the action object, and confirmations are tested with known answer probes and a weekly sample so that ground truth is a decision and not a reflex; every confirmed outcome becomes memory the next exception reads. ## Sources - [Industrial Data Ontology for a Cold Chain From Plant to Store in the United States](https://codeatoms.ai/cold-chain-ontology-us/) (United States) - [Sovereign AI Architecture for a Freight Corridor and Customs Clearance in Saudi Arabia](https://codeatoms.ai/freight-corridor-customs-saudi-arabia/) (Saudi Arabia) - [A Self-Improving AI System for Carrier Exception Handling in the United States](https://codeatoms.ai/carrier-exceptions-us/) (United States)