Predict arrival times for deliveries and journeys

Logistics and transport companies predict arrival times precisely

Needs setupAI Act: no extra obligationsForecasting from figuresMobility & logistics

Fits: Customer service · Logistics & warehousing

Customers and passengers get a reliable time window instead of “sometime during the day”. Fewer enquiries, fewer failed delivery attempts, better planning at the loading bay.

Is this for you?

  • You have planned and actual times from routes, journeys or telematics for several months.
  • Customers or passengers often ask when you will arrive.
  • Today’s time windows are too wide or are often missed.
First step today

Export planned and actual arrival times from eight weeks of routes with time window, day of the week and number of stops, without driver names, number plates and customer addresses. Use the template below to show when and by how much you are typically late.

How to go about it

  1. Decide which arrival is predicted and how precisely
  2. Collect planned, actual and influencing data
  3. Have arrival times predicted
  4. Compare with actual arrival times
  5. Give time windows to customers and dispatch
  6. Retrain for changed routes and roadworks

Templates to copy

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Plan the rollout
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Included in the training

The complete guide

  • 6 steps explaining exactly what to do
  • 3 proven templates to copy into your own AI
  • Safe use: data, checking, deciding, informing
  • Pitfalls, effort and how to recognise success

With the training you get all 430 guides for as long as your access runs.

See the trainingOne-off per person, no subscription

Same approach, other use cases

22 more use cases follow the pattern “Forecasting from figures”.

Assess storm and flood risk per addressDevelop and test trading strategies with machine learningEstimate market-rate salaries for positionsEstimate yields and plan harvest and marketingForecast clients’ demand for skilled workersForecast crowds and waiting timesForecast liquidity for the coming weeksForecast material requirements and suggest reordersForecast passenger numbers and adjust frequencyForecast revenue from the pipelineForecast sales and plan stockForecast staffing needs from care grades and occupancy
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G-ML-02 · Legal status 08.10.2026