Case study · AerospaceGeanonimiseerd op verzoek. Genoemde referenties onder NDA.
Manufacturing · Robotics

From managing robots to optimising an intelligent fleet.

A manufacturer running AMRs and AGVs had limited visibility into route congestion, charging downtime, idle fleet utilisation and cross-zone movement inefficiencies.

Manufacturing · RoboticsAMR / AGV fleet intelligenceHybrid RTLS / RFID + analytics · measured outcomes.live asset positionHigherAMR/AGV utilisationLageridle charging time
De uitdaging

Waar ze tegenover stonden.

Route congestion

Robotic traffic created congestion hotspots across zones.

Idle & charging downtime

Fleet utilisation and charging were unmanaged.

No fleet-level view

Robots were managed as individual machines, not a fleet.

Onze aanpak

Leverancier-neutraal, resultaat-geleid.

UWB for real-time robotic positioning, BLE for asset-interaction tracking, AGV/AMR telemetry for route analytics, and AI orchestration for congestion, idle-time and predictive movement optimisation.

Hoe we het opgelost hebben.

Wat we geleverd hebben.

  • UWB RTLS real-time robotic positioning
  • BLE asset-interaction tracking
  • AGV/AMR telemetry route analytics
  • AI fleet orchestration — congestion, idle, predictive optimisation
Resultaten

Het resultaat dat het bestuur zag.

Higher
AMR/AGV utilisation
Lager
idle charging time
Veiliger
robotic traffic
Higher
throughput
“We stopped managing robots as individual machines and started optimising them as an intelligent fleet.”

— Director of operations, industrial manufacturing (client anonymised)

Gebruikte technologie

De stapel erachter.

UWBBLEAGV/AMR telemetryAI orchestration
Kan dit je programma zijn?

Vertel ons waar je vastzit.

Dertig minuten op uw use case, de nummers, en wat zou eigenlijk uw (1931(20) bewegen. Leverancierneutraal, geen platform.

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