AI for Logistics & Supply Chain

Route planning is mostly an optimisation problem: given stops, time windows, vehicle capacities and driver-hour rules, find a good plan. Mature optimisation solvers do that well, and they do not need a neural network. Where machine learning adds value is in the inputs - realistic travel and service times by area and time of day, likely dwell at each customer, and how much volume to expect tomorrow.

We combine the two. Forecasting models estimate demand and timings, the optimiser builds the plan, and dispatchers can lock stops, move jobs and re-run in seconds. The same historical data drives ETA prediction, so customers receive arrival windows that reflect how your network actually performs rather than a straight-line estimate.

Freight still moves on documents - bills of lading, commercial invoices, packing lists, delivery notes and proof-of-delivery photos - arriving as PDFs, scans and email attachments in every layout imaginable. Modern document AI combines OCR with language models to extract shipper, consignee, references, line items, weights and charges into structured fields, even from layouts it has not seen before.

The important design choice is what happens next. Every extracted field carries a confidence level and checks against your own data - does the order exist, do the weights reconcile, does the invoice match the agreed rate? Clean documents flow straight into your TMS or finance system; anything uncertain lands in a review queue with the original next to the extracted values, so staff correct exceptions instead of retyping everything.

A large share of logistics service contacts ask where a shipment is, when it will arrive or how to change a delivery. An AI agent connected to your tracking and order systems can answer those questions accurately, in natural language, on chat, email or voice - and hand the conversation to a person with full context when it cannot help.

The same approach works internally. An operations agent can watch for exceptions such as missed scans, late pickups or temperature excursions, summarise what happened, and propose the next action for a planner to approve. We limit what agents are allowed to change, log every action, and keep people in charge of decisions that affect customers or cost.

Every model on this page learns from operational events - GPS and telematics pings, TMS milestones such as picked up and delivered, WMS scans, carrier status messages and proof-of-delivery timestamps. In most networks these exist but disagree: carriers report in different formats and time zones, scans arrive late or not at all, and the same stop has three addresses. The first phase of a logistics AI project is usually joining those feeds into one clean event history per shipment.

That history also gives you the baseline. Before any model goes live we measure how far today's promised arrival times are from actual arrivals, by lane, customer and carrier. ETA models are then judged on the same measure - the spread of errors and how often a delay warning came early enough to act on, not a single headline accuracy figure. Where a lane has too little history, the system should say so rather than show false precision.