Automate the repeatable
Enter the shipment, propose a carrier on a known lane, check the price against your own history, match the documents.
Artificial Intelligence
Artificial
Artificial
Intelligence in
logistics
AI is designed here as operational support: decide faster, automate cleanly, and relieve processes where it counts.
Explainer · approx. 45 secondsHow AI relieves logistics operations
- Data
- Detect
- Decide
- Automate
- Relieve
AI as operational support – decide faster, automate cleanly, relieve in a targeted way.
Automatic · pause anytime
AI approach
Exceptions stay with people
The deadline that slips; the carrier who does not answer; the cargo that is not what was announced.
Measure against your own practice
A method that does not beat existing dispatching is not progress — however modern it sounds.
Sustainability
Efficiency through AI is sustainability in practice
Every kilometre AI saves lowers cost and emissions at once – efficiency and sustainability are the same movement here. Sustainability is therefore not a separate programme but the direct result of better dispatching. It only sustains itself if it is commercially sound, however: there is no sustainability without matching profits. Only a profitable company can invest in modern fleets and better networks.
Fewer empty kilometres
AI detects round trips and avoidable empty runs in real tour data – the most effective lever for cost and CO₂ alike.
Higher utilisation
Consolidation and an optimal stop sequence deliver more payload per kilometre driven – the same output with fewer resources.
Reliable figures
CO₂, cost and operational impact are assessed together so that measures stay commercially viable.
Context
AI is not an end in itself
In many companies, AI is discussed as a topic for the future, even though operational data, processes or roles are not yet sufficiently structured. That is why LOG|CONS deliberately connects AI with existing workflows and decision paths.
Documents
Document processing reduces manual steps in order and information flows.
Recommendations
Historical data and rules are distilled into concrete decision support.
Detection
Anomalies in pricing, empty runs or performance become visible faster.
Sequence · about 45 seconds
How a method you can trust comes about
The basis is completed cases — only there is the eventual outcome known.
Runs automatically · can be paused at any time
In detail
What automation in dispatching actually means
What can be automated — and what cannot
A dispatcher's day consists largely of tasks that are not decisions. They are repeatable, documented and therefore automatable.
- Automatable: Enter the shipment, propose a carrier on a known lane, check the price against your own history, match the documents, update the status.
- Not automatable: the deadline that slips; the carrier who does not answer; the cargo that is not what was announced.
„AI takes the routine, people decide the exception“ is therefore not a reassuring phrase but a division of labour along a single question: is the task repeatable?
Why clean models fail against real data
An example from my own work: round trips were to be detected geographically — unloading point close enough to the next loading point, so the trips belong together.
On real trip data the chain broke constantly, because in between the trailer goes off to be washed, repaired or repositioned. Geographically a break, economically the same round trip.
What works is less spectacular: same trailer, continuous in time, closed on the return to the country of origin.
The order that holds up
- Reconstruct: Work out from completed cases how decisions were actually made.
- Recalculate: Test against periods the method did not see while learning.
- Propose: Visible as a proposal, with a reason, open to being overruled.
- Take over: Only once the hit rate is proven and a wrong call is cheap to correct.
How to recognise a method that holds up
- It was tested against the result actually achieved — and against today's way of working. What does not beat existing practice is not progress.
- It says when it is uncertain. A proposal without a reliability figure forces every single one to be checked — then work was shifted rather than taken away.
Frequently asked questions
AI and automation in dispatching
What can realistically be automated?
Repeatable tasks: order entry from structured input, carrier proposals on known lanes, price checks against your own history, document matching. Not automatable is anything requiring negotiation or judgement between conflicting goals.
Does AI replace the dispatcher?
No, it shifts their work. What falls away is the part that follows rules anyway and eats the most time. What remains is the exception — the case with no precedent in the data.
How do you know a method is better?
A backtest over your own history, on periods it did not see while learning, with a clear benchmark: how well does the existing method hit, how well the new one?
How much data does it take?
Less than assumed, but cleaner. What matters is that the outcome is known. Ten thousand orders without a reliable closing are worth less than a few thousand with one.
Why do such projects fail?
Because the model represents how the business should run rather than how it does. Second reason: no traceability — a proposal without a reason creates checking work instead of taking it away.
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