Deep Learning × Logistics

AI in 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, with the systems you already run.

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

Logistics 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
€0,9M
Savings potential/year
Flexible
Pilot timeframe
The 6 modules

Each module solves a concrete logistics 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.
€132.300
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.
€98.820
Savings / year
XGBoostPredictive Maint.
Module 03
Driver turnover
Random Forest + survival analysis shows at group level where resignation risk is building, based on shift plans, telematics and overtime data.
€53.900
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.
€243.000
Savings / year
DBSCANRoutenoptimierung
Module 05
Diesel consumption anomalies
Isolation Forest detects which truck is using far more fuel than expected on which route and points to the cause: technical, route or driver.
€161.688
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.
€208.800
Savings / year
LSTMZeitreihen
Savings potential by module: full overview
Interactive

The Logistics Simulator

S01-S04
Four simulations, all assumptions disclosed
Empty runs, dwell time at the ramp, misplanning costs as a distribution and the driver retention funnel: set the sliders to the scale of your fleet. Every calculation path sits beneath its simulation.
Open all four simulations →
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
We connect what already exists in your systems: telematics (Fleetboard, TomTom, Trimble), ERP, TMS, fuel cards, HR. 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 →
Evidence & Sources

Where the industry figures come from

The levers of the model scenario are anchored in published studies and industry data. Every third-party figure cited in the text is referenced here.

  • BGL cost model: long-haul 130,000 km/year, consumption 33-34 l/100km, diesel share 30% and above.
  • EU FTL rates 2026: €1.10-2.00/km.
  • Eurostat 2024: 21.6% of EU road-freight km run empty (Germany int. 20.2%).
  • Demurrage practice: €30-80/h, courts consider €50-60/h reasonable.
  • ATRI Operational Costs: tyres 4.7-5.0 US cents per mile.
  • ATA TMC: 20% underinflation = -30% tyre life.
  • Uber Freight (vendor claim): 10-15% fewer empty miles.
  • Published eco-driving programmes: 4-15% lower fuel consumption among coached drivers.
  • Correll et al. 2024 (Expert Systems with Applications): driver-turnover prediction from operational data, 60-70% accuracy, 50-60% recall.
  • ATA turnover rates USA: LTL ~12%, truckload over 90%.
  • UGPTI/FreightWaves: replacement cost per driver ~$6,000-12,000.
  • McKinsey: AI forecasts reduce errors by 20-50%.

Figures from other markets serve as plausibility anchors and are not transferred onto the model fleet. All EUR amounts are model calculations based on the assumptions stated in the disclaimer. Source retrieval date: 2026-08-15.

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 are 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.