🚚 Logistics AI - Supply Chain Intelligence

Logistics AI that survives
the dispatcher and the regulator

AI systems for fleets, warehouses and 3PLs - route optimization, demand sensing, predictive maintenance, WMS automation. Every recommendation explainable, every model documented. Built for ELD, ISPS / IMO and GDPR for driver data.

Consultation call
⚠️ Regulated domain

Logistics AI is not a black box

Every decision needs an explanation - for the regulator, for the user and for the internal auditor.

100%
Explainable Decisions
ELD-aware
fleet design
ISPS + IMO
maritime
Audit-aware
design
⚙️ Logistics AI solutions

End-to-end AI for the sector

🗺️

Route Optimization

Vehicle routing with realistic constraints.

  • Time windows + service times
  • Driver hours-of-service (HOS)
  • Multi-depot + backhaul
  • Live re-routing on events
📊

Demand Sensing

Short-horizon demand sensing for replenishment.

  • Daily / hourly demand sensing
  • External signal fusion (weather, events)
  • Pipeline integration with WMS / ERP
  • MAPE reported per lane / SKU
🔧

Predictive Maintenance

Failure prediction for fleet and warehouse equipment.

  • Sensor fusion (vibration, temperature, oil)
  • Remaining-useful-life (RUL) modelling
  • Maintenance scheduling integration
  • Cost of false-positive modelled
📦

WMS Automation

Slot allocation, pick path optimization, exception detection.

  • Slot allocation by velocity + weight
  • Pick path optimization
  • Damage / mispick detection
  • ERP / WMS integration
🛡️

Driver Data Privacy

GDPR-compliant telematics handling.

  • Personal-data minimization
  • Driver consent workflow
  • Right to access + portability
  • Retention controls
🔐

Model Governance

Documentation, monitoring and rollback of every production model.

  • Model registry with lineage
  • Drift detection on demand signals
  • Champion / Challenger flow
  • Reproducible feature engineering
📋 Regulation

Built for every regulator in the room

🇺🇸

US: ELD, FMCSA, DOT, CCPA

FMCSA Electronic Logging Device (ELD) mandate, DOT hours-of-service compliance, CCPA for driver data in California, NIST cybersecurity guidance for connected vehicles.

🇪🇺

EU: GDPR (driver data), eCMR, ISPS, IMO

GDPR for driver and shipper data, eCMR digital consignment notes, ISPS Code for port-facility security, IMO 2021 cybersecurity guidelines for ship management.

🌍

Operational controls + audit trail

Sensor-data integrity, segregation of duties, audit trail for every routing / dispatch decision - built to support customer audit and regulator inspection.

📊 Industry benchmarks

What to expect from Logistics AI that works in production

Most models fail not in the demo - but in production: drift, audit, integration. Here is what the public industry research reports and where our architectural contribution comes in.

🗺️

Route optimization fuel + time savings

What the industry reports: Top-quartile fleets report 10-15% fuel reduction + 8-12% time reduction with realistic-constraint vehicle routing vs naive Google-Maps baseline (Gartner supply chain 2024).

Where we come in: Time windows + service times, driver HOS, multi-depot + backhaul, live re-routing on events - constraints honoured, not 'closest first'.

  • Time windows and service times honoured
  • Driver HOS compliance (not just shortest path)
  • Multi-depot and backhaul
  • Live re-routing on traffic + cancellations
🔧

Predictive maintenance MTBF lift

What the industry reports: Sensor-fusion predictive maintenance increases mean-time-between-failures by 20-35% for fleet and warehouse equipment vs interval-based maintenance (Bain industrial 2024).

Where we come in: Sensor fusion (vibration, temperature, oil), remaining-useful-life modelling, maintenance scheduling integration, cost of false-positive modelled - not just 'replace at threshold'.

  • Sensor fusion (not single-signal)
  • RUL modelling (not threshold heuristic)
  • Maintenance scheduling integrated with WMS / ERP
  • Cost of false-positive modelled
📊

Demand sensing forecast error

What the industry reports: Daily / hourly demand sensing combined with external signals reduces lane-SKU forecast error by 20-30% vs weekly baseline (McKinsey supply chain 2024).

Where we come in: Daily / hourly demand sensing, external signal fusion (weather, events), pipeline integration with WMS / ERP, MAPE reported per lane / SKU.

  • Daily / hourly granularity (not weekly)
  • External signal fusion (weather, events)
  • Pipeline integration with WMS / ERP
  • MAPE reported per lane / SKU

* SLAtech, since 2004. We have worked with fleets, 3PLs and warehouse operators in 14 countries. Commercial examples available on a consultation call.

Ready to build Logistics AI that the dispatcher actually trusts?

30-minute consultation call - we map the supply-chain use cases, propose an architecture, and quantify ROI.