Deep Learning × Logistics

Your data knows more
than your dispatch team suspects

6 modules. 6 problems every fleet manager knows. 6 data-driven solutions that turn your existing telematics, ERP and HR data into concrete euro figures - no new sensors, no new ERP.

Total savings potential per year
€0
For a fleet of 150 trucks · calculated conservatively
At a glance

Numbers that speak for themselves

Based on a typical mid-sized logistics company with 150 trucks, 43 regular customers and EU-wide transport.

6
Modules
150
Trucks analysed
€1.16M
Savings potential/year
Flexible
Pilot timeframe
The 6 modules

Each module solves a concrete problem

Click on a module to view the complete interactive notebook with code, visualisations and business impact.

Module 01
Dwell-time prediction
An LSTM model predicts how long your truck will wait at which loading bay - and identifies which customers are systematically slowing down your fleet.
€184.300
Savings / year
LSTMTelematik
Module 02
Tyre-wear prediction
Gradient boosting infers from route profile, load and position when each tyre is due for replacement - before the workshop notices.
€118.770
Savings / year
XGBoostPredictive Maint.
Module 03
Driver turnover
Random Forest + survival analysis identifies which drivers will resign within 90 days - from shift plans, telematics and overtime data.
€254.600
Savings / year
Random ForestHR-Analytics
Module 04
Empty-run analysis
DBSCAN clustering uncovers recurring empty-run patterns in your route data - and shows where consolidation genuinely pays off.
€220.700
Savings / year
DBSCANRoutenoptimierung
Module 05
Diesel consumption anomalies
Isolation Forest detects which truck consumes unexpectedly high fuel on which route - and points to the cause: technical, route or driver.
€96.260
Savings / year
Isolation ForestAnomaly Detection
Module 06
Order-volume forecasting
A multi-target LSTM forecasts order volume per customer 4 weeks ahead - enabling proactive rather than reactive capacity planning.
€283.100
Savings / year
LSTMZeitreihen
Savings potential by module - full overview
Our approach

No theory - results in weeks

We work with the data you already have. No vendor lock-in, no cloud mandate, no hidden costs.

📊
Your data, your systems
Telematics (Fleetboard, TomTom, Trimble), ERP, TMS, fuel cards, HR - we connect what already exists in your systems. No new system required.
⚡
Results before perfection
We deliver a pilot dashboard with real numbers from your data. No 6-month concept - you see the ROI before you invest.
🔧
Industry knowledge + data science
We understand dispatch, fleet management and the driver shortage. Our models solve real problems - not academic exercises for the proof-of-concept graveyard.

Ready to put your data to work?

Schedule a data workshop →
Note on the context of this portfolio

The following six modules use a fictitious mid-sized logistics company (150 trucks, EU-wide transport, 43 regular customers) to illustrate how deep learning and AI can be applied across the entire operational logic - from dwell-time prediction to order-volume forecasting.

All figures, datasets and results are entirely fictitious. They serve solely to illustrate the methodology and the type of impact achievable - no promises made.

In a real project, the domain expertise of your company is the decisive factor: what telematics data is available? How does your dispatch process work? Where are the biggest cost drivers - empty runs, dwell times or driver turnover? On this basis we develop models tailored to your reality.

This portfolio shows what questions data science can answer in the logistics industry - the concrete answers emerge only with your data.