AI in Supply Chain and Logistics
Forecasting, exception handling and trade documents on your own data.
Supply chain teams are drowning in exceptions and starved of forecast accuracy. Both problems are data problems that the existing planning systems were not designed to solve, because they assume clean structured inputs and the real world sends PDFs and email. Senteras builds on the shipment, order and supplier history the business already has.
Book a free consultationWhat makes this hard
- Planners spend their day on exceptions rather than planning, and exception volume grows faster than headcount
- Forecast error drives either excess inventory or stockouts, and static models do not adapt to demand pattern shifts
- Trade and customs documentation is manual, high-volume and penalised when wrong
- Supplier risk is discovered when a shipment fails to arrive rather than in advance
Where AI earns its place
Demand Forecasting
Replace static models with systems that incorporate external signals and adapt to pattern shifts, forecasting at the level decisions are actually made.
Materially lower forecast errorException Management
Classify and resolve routine exceptions automatically and route only genuine decisions to a planner, with the reasoning shown.
Planner time returned to planningTrade Document Processing
Extract and validate data from commercial invoices, packing lists, bills of lading and certificates of origin across formats and languages.
Customs documentation without rekeyingSupplier Risk Monitoring
Continuously monitor supplier financial, geopolitical and logistics signals and flag risk before it becomes a production stoppage.
Warning ahead of disruption, not afterFigures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
The rules that shape the build
These are the constraints that decide the architecture, usually before anyone has picked a model. This is general information about the regulatory landscape, not legal advice on your obligations.
Customs and trade compliance
Automated classification and valuation still carry importer-of-record liability. The model proposes, a licensed broker decides.
UFLPA and forced labor rules
Supply chain mapping obligations extend beyond tier one, and an AI-assisted map is only as good as the documents behind it.
FSMA (food supply chains)
Traceability recordkeeping requirements apply to the records the model reads, and they must remain retrievable independently of it.
Forecast at the decision level, not the reporting level
The most common failure in supply chain AI is forecasting at the level the business reports on rather than the level it decides on. A monthly national forecast is accurate and useless when replenishment happens weekly per location. Senteras scopes the forecast granularity to the decision it feeds, which usually means accepting a worse headline accuracy number in exchange for a forecast that changes what anyone does.
Where this applies
The same core systems, with the differences that matter in each setting.
- Third-party logistics
- Exception management across many client accounts, where tenant isolation is the hard part.
- Freight brokerage
- Carrier matching and rate benchmarking against your own booking history.
- Trucking and fleet
- Maintenance prediction and dispatch optimization.
- Warehousing
- Slotting optimization and labor forecasting against inbound schedules.
- Maritime and ports
- Documentation processing at volume, plus berth and yard planning.
- Procurement teams
- Contract term extraction and spend classification across a supplier base.
Common questions
Our planning system already forecasts. Why add anything?
Planning systems assume clean structured inputs. The exceptions that consume your planners' day arrive as email, PDFs and phone calls, which is precisely the gap. The forecast improvement is usually secondary to the exception handling.
Can AI classify customs entries?
It can propose classifications and flag inconsistencies. Importer-of-record liability does not transfer, so a licensed broker decides. Presenting a proposal as a decision is how this use case creates penalty exposure rather than removing it.
How do you handle multi-tenant work at a 3PL?
Tenant isolation is enforced at retrieval, so no client's data can surface in another's context. It is the hardest part of a 3PL deployment and the part worth spending the engineering time on.
What granularity should we forecast at?
Whatever level the replenishment or production decision is made at. Forecasting at reporting granularity produces a better headline number and changes nothing anyone does.
How we build it
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
Model Fine-Tuning & Integration
Models that speak your industry's language, trained on your data.
Local & On-Prem LLM Deployment
The most powerful AI models, running entirely on your hardware.
Demand Forecasting
Forecast at the level decisions are made, not the level you report on.
Document Processing
Extraction, classification and validation across formats you do not control.
Invoice Processing
Reads any layout, and tells you when it is unsure.
Start with a conversation, not a proposal
Thirty minutes. We will tell you what we would change first, and whether you need us at all.
Book a callThe firm behind the firm