AI for Logistics
In logistics AI goes where the scale is: routes, stock levels and shipping paperwork. Planning systems recalculate routes against traffic and breakdowns, predictive models forecast demand a fortnight out, and OCR with a language model transcribes consignment notes and invoices instead of a dispatcher. Hauliers report fuel costs down by as much as 20% and shorter order lead times. All of it rests on the data coming out of the TMS and the telematics - the model plans with what it is given, and gaps in the GPS record show up in the results immediately.
Three uses of AI in Logistics
Route and delivery-schedule optimisation
The system reads traffic, weather, delivery windows and vehicle capacity and builds routes out of them. It accounts for shipment priorities, weight limits and time slots, and when a jam or a breakdown appears it recalculates the route.
Warehouse demand forecasting
The model forecasts demand per product a fortnight ahead from sales history, seasonality, market trends and weather. It raises replenishment orders itself and rearranges the warehouse by pick frequency.
Shipping documents read and classified automatically
OCR with an NLP model reads and classifies waybills, consignment notes, invoices and customs paperwork. It pulls out sender, consignee, weight and dimensions, checks them against the orders in the TMS and sets discrepancies aside for an operator.
Recommended stack
Return on investment
30 h
Hours saved weekly
€20
Hourly rate
€30,000
Annual saving
The maths: 30 h/week × €20/h × 48 weeks = €30,000 a year
Figures are quoted in euro, converted from Polish złoty at a fixed rate of 4.30 PLN to 1 EUR and rounded. Contracts are settled in either currency.
What makes it hard
Connecting to TMS and WMS systems that often still exchange data in ageing formats (EDI, XML)
The quality of GPS and telematics data - gaps, transmission delays and formats that differ between suppliers
Road conditions and weather move enough that optimisation models need continuous retraining
The planner has to stay inside drivers' hours rules (Regulation (EC) 561/2006)
Frequently asked questions
How would AI bring our transport costs down?
Routes shorten by 15-25% of distance run, demand forecasting removes empty legs, and paperwork stops taking a dispatcher half a day. A company running 20 or more vehicles typically saves €35,000-93,000 a year on fuel and dispatcher time.
Do we need a large IT department for this?
No. As an outside integrator we deliver a system that connects to your current TMS or WMS over its API. On your side one technical person is enough to coordinate. We can take on maintenance too, under an SLA.
What data does it need?
As a minimum: twelve months of order history, vehicle GPS data and a current delivery-address base. The more history the better - two to three years is ideal. The data can come from a TMS, from spreadsheets or even off paper; we will help collect and clean it.
AI in Logistics
Tell us what gets done by hand at your company, and how often. Within 24 hours you get back where to start and how long it takes.
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AI readiness audit
Ten questions about processes, data and people. The result appears on screen.