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 - from 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 consumes unexpectedly high fuel 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
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 →
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 - they 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 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.