AI for Logistics & Transportation
Build route planning, delivery estimates and reviewed exception workflows around operational constraints, representative evaluation and dispatcher ownership.
Discuss a logistics AI pilot →Define the dispatch decision and current baseline
TensorBlue scopes logistics AI development around route planning, delivery-time estimates and reviewed operational exceptions. Start with the decision dispatchers need to make, the current planning method and the person who can approve a change. A routing solver and a delivery-time model solve different problems; neither should be judged by a generic accuracy promise.
Agree the service area, fleet, planning horizon and acceptance criteria. Record operating costs and the current level of service before a pilot. A proposal should state interfaces, evaluation deliverables and dependencies. Improvements depend on actual demand and constraints; no fixed saving or payback is promised.
Represent route constraints before optimizing
Prepare consistent stop identifiers, coordinates, service durations, vehicle capacities, depots and delivery windows. Record unavailable roads, vehicle restrictions and dispatcher overrides where the project has reliable inputs. Distinguish hard constraints from preferences that can be traded against distance or lateness.
Compare a proposed plan with the current dispatch baseline on the same orders and constraints. Report unassigned stops and infeasible plans explicitly. Do not hide a missed delivery window inside an average distance metric. Review the plan with dispatchers before publishing assignments, and retain the existing planning route when required inputs are unavailable.
Evaluate delivery estimates using time-aware evidence
Define the prediction event: an estimate made before departure differs from an updated estimate made during a trip. Separate arrival time from service completion and document timestamp quality, time zones, cancelled stops and missing location updates. Use only information available at the moment the estimate would be issued.
Backtest across historical periods and report error by route, service area and operating condition. Compare against existing estimates and a simple baseline. Show uncertainty or an exception flag when evidence is sparse; an average result does not establish reliable performance for every delivery. Customer notifications should identify the latest update and avoid presenting a stale estimate as live.
Keep operational exceptions reviewable
Define which events trigger a proposed reroute, an alert or a dispatcher review. Account for assignments already in progress and explain which orders a revision would affect. Establish approved access and retention for location and operational records, with outputs limited to the purpose agreed for the pilot.
Treat vehicle-maintenance flags as supporting evidence for the responsible operator, with escalation based on the available maintenance history. Do not infer a guaranteed breakdown reduction or use an opaque driver score as a substitute for contextual review. Warehouse picking and replenishment workflows require their own inputs, constraints and acceptance criteria.
Design interfaces and recovery with the operator
Confirm the available transport and warehouse system interfaces, authentication, permissions, identifiers and update frequency. An integration assessment should establish what can actually be read and written rather than assume every platform supports the same API. Agree an output contract for plans, estimates and exception reasons.
Test delayed updates, duplicate events, partial assignment failures and unavailable mapping services. Make retries safe and preserve a visible record of accepted versus proposed plans. A handover can include evaluation results, interface configuration, monitoring and a recovery runbook. Name the owners who can approve updates and restore the established workflow.
Prepare a representative logistics pilot
In a hypothetical dispatch pilot, an operator could compare current plans with proposed routes for one service area while measuring feasible assignments, distance, late stops and dispatcher overrides. Historical replay can identify problems before a controlled operational trial. This is an evaluation example, not a TensorBlue client result or a promised saving.
Bring an authorized sample of orders, route and event history, vehicle constraints, current tools and an operational owner. Identify missing data and interface access before estimating cost or schedule. Separate implementation, mapping or infrastructure charges and ongoing support in the proposal; expand only after the agreed evidence supports the next scope.
Frequently Asked Questions
How is route-planning improvement evaluated?
Compare proposed and current plans on the same orders, fleet and constraints. Report feasible assignments, distance, late stops and dispatcher overrides. No fixed saving or payback is promised.
Can a logistics pilot integrate with our TMS or WMS?
An integration assessment must confirm available interfaces, permissions, identifiers and update frequency. The scope should define approved read/write actions, failure handling and the dispatcher fallback before implementation.
What data is needed for delivery-time estimates?
A scoped assessment reviews order and route events, timestamp quality and permitted operational signals. Evaluate estimates at the moment they would be issued, report uncertainty and retain a fallback for missing or stale inputs.