AI Solutions for Logistics and Supply Chain Operators
Route optimisation, demand forecasting, ETA prediction, warehouse intelligence and freight document automation, built on your own shipment history and connected to your TMS, WMS and ERP. Fewer empty miles, fewer stockouts, less paperwork.
- Dynamic route and load optimisation
- SKU and lane-level demand forecasting
- Predicted ETAs and exception alerts
- Slotting and labour planning for warehouses
- Bill of lading, invoice and customs extraction
Proof of concept in 2–4 weeks. Production release in 6–14 weeks.
What is AI in logistics?
AI in logistics is the use of machine learning and optimisation to plan and run the movement and storage of goods: forecasting demand, routing vehicles, predicting arrival times, planning warehouse work and processing freight documents. The best early returns come from targeting one costly pain point, such as forecasting or exception handling, rather than deploying AI everywhere at once.
Shortest-path routing and spreadsheet forecasts cannot handle live traffic, driver hours, delivery windows and volatile demand together. Models trained on your own lanes, customers and warehouses can, and they get better as your data grows. The work is in connecting them to the TMS and WMS where planners and dispatchers already make decisions.
Last reviewed by the Perimattic AI engineering team.
How is AI used in logistics?
Six use cases account for most of the value. Each one targets a cost logistics teams already measure: miles, late deliveries, inventory, labour hours or detention.
Route and load optimisation
Routing that weighs live traffic, delivery windows, vehicle capacity, driver hours and customer priority, and re-plans when orders or conditions change mid-day.
Data it needs: Stops, time windows, vehicle specs and historical drive times
Demand forecasting
Forecasts by SKU, location and lane that account for seasonality, promotions and external signals, so inventory and capacity are in the right place before demand arrives.
Data it needs: Two or more years of orders, shipments and inventory
ETA prediction and exceptions
Arrival-time models built on telematics and history, with early alerts for shipments likely to miss their window so customers hear before they ask.
Data it needs: GPS or ELD pings, milestones and delivery history
Warehouse intelligence
Slotting, labour forecasting and pick-path optimisation that cut travel time and match staffing to the inbound and outbound plan.
Data it needs: WMS transactions, locations and labour records
Freight document automation
Extraction from bills of lading, proof-of-delivery, carrier invoices and customs forms into your TMS or ERP, with automatic matching and exception queues.
Data it needs: Sample documents for each document type and carrier
Fleet maintenance and safety
Models that predict vehicle component failures from telematics and fault codes, and flag risky driving patterns for coaching.
Data it needs: Telematics, fault codes and maintenance records
Which logistics AI use case should you start with?
Freight document automation and ETA prediction usually show results first: the data already exists and the saving is easy to count. Route optimisation and network-level forecasting deliver more value but need cleaner master data and planner buy-in.
| Use case | Data it needs | Time to first result | Watch out for |
|---|---|---|---|
| Freight document automation | Sample documents per type | 2–4 weeks | Carrier format variation; handwritten PODs |
| ETA prediction | Telematics + milestones | 4–6 weeks | Gaps in GPS data; inaccurate appointment times |
| Demand forecasting | Orders + shipments history | 4–8 weeks | Stockouts hiding true demand in the history |
| Warehouse slotting | WMS transactions + locations | 6–10 weeks | Physical constraints not captured in the WMS |
| Route optimisation | Stops, windows, fleet data | 8–12 weeks | Driver adoption; unrecorded customer constraints |
Built for driver rules, customs data and your operational systems
Logistics AI has to respect the rules drivers and shipments run under, and fit into a stack of TMS, WMS, ERP and telematics platforms. These are the constraints we build in.
Hours of service
FMCSA HOS and EU Regulation 561/2006 limits built into routing as hard constraints, not suggestions.
Customs and trade data
Extraction and validation for CBP ACE, ICS2 and UK CDS filings, with human review before submission.
Dangerous goods
ADR, IMDG and IATA DGR restrictions respected in load planning and routing.
GDPR and driver privacy
Telematics and driver data minimised, purpose-limited and access-controlled.
ISO 28000 and TAPA
Supply chain security requirements reflected in data access and high-value load handling.
SOC 2
Access control, change management and audit evidence for shipper and customer due diligence.
Typical systems in scope
We design and document systems to meet these requirements. Certification and regulatory sign-off stay with your organisation; we supply the evidence your reviewers ask for.
From scoping call to production in five stages
Every engagement follows the same staged process, priced before the work starts and measured against a baseline agreed up front.
- 11–2 weeks
Scoping and data assessment
Use case defined, data readiness scored, compliance constraints listed, and a fixed quote agreed.
- 21 week
Baseline and evaluation design
Today's accuracy, cycle time or loss rate measured, and success thresholds agreed in writing.
- 32–8 weeks
Model development
Data preparation, training and iteration against a held-out test set, with weekly demos on real data.
- 42–4 weeks
Integration
Inference wired into the systems your teams already use, with auth, audit logging and fallback paths.
- 5Ongoing
Production and monitoring
Deployment, drift and accuracy monitoring, alerting, retraining and runbooks for your operations team.
For logistics operators, the baseline stage measures today's on-time rate, cost per mile, forecast error or documents per hour, so the business case is proved in your own numbers.
What makes Perimattic a fit for logistics AI
Much of logistics runs on documents and forecasts. These case studies show the document AI we have put into production, and the principles we apply to every logistics engagement.
Optimisation and ML together
Routing and scheduling need solvers as well as predictions. We combine forecasting models with operations research so plans are both accurate and feasible.
Connected to your TMS and WMS
Outputs flow into the planning and execution systems dispatchers already use, through APIs or EDI, rather than into a separate dashboard.
Planners stay in control
Recommendations come with reasons and can be overridden, and overrides feed back into the model so it learns your operation.
You own the system
Models, code and data pipelines are yours, deployed in your cloud, with no per-shipment platform fees.
Services behind these logistics systems
AI in logistics: frequently asked questions
How is AI used in logistics?
Logistics companies use AI for route and load optimisation, demand forecasting, ETA prediction and exception alerts, warehouse slotting and labour planning, freight document automation, and fleet maintenance. Forecasting and document automation are common starting points because the data already exists.
How does AI route optimisation differ from standard routing software?
Standard routing finds short paths between stops. AI-based routing also predicts real drive and service times from your history, accounts for live traffic, delivery windows, vehicle capacity and driver hours, and re-plans during the day when orders or conditions change.
How does AI improve demand forecasting in the supply chain?
Machine learning models forecast demand by SKU, location or lane using order history, seasonality, promotions and external signals such as weather or events. Better forecasts cut stockouts and excess inventory at the same time, and help plan transport capacity ahead of peaks.
Can AI predict delivery ETAs accurately?
Yes, when it is trained on your own telematics and delivery history. ETA models learn how long each lane, customer and time of day really takes, and update predictions as the truck moves, so customers and planners get early warning of late shipments.
Can AI process bills of lading, PODs and freight invoices?
Yes. Document AI extracts fields from bills of lading, proof-of-delivery, carrier invoices and customs forms, matches them against orders and rates, and sends only exceptions to a person. One of our document automation clients cut manual processing by 67%.
Will AI integrate with our TMS and WMS?
Yes. We connect through platform APIs, EDI messages such as 204, 214 and 210, or database integrations, so forecasts, routes and extracted data appear in the systems your planners and warehouse teams already use.
How long does a logistics AI project take and what does it cost?
A proof of concept for one use case typically takes 2–4 weeks and starts from $3,500. A production system with TMS or WMS integration and monitoring takes 6–14 weeks and typically costs $10,000–$30,000. Network-wide optimisation projects are quoted after scoping.
