Case study · AerospaceAnonimo su richiesta. Riferimenti denominati sotto 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 utilisationIn bassoidle charging time
La sfida

Quello che stavano contro.

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.

Il nostro approccio

Vendor-neutral, esito-led.

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.

Come abbiamo risolto

Quello che abbiamo consegnato.

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

L'esito che il consiglio ha visto.

Higher
AMR/AGV utilisation
In basso
idle charging time
Sicuro
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)

Tecnologia utilizzata

Lo stack dietro di esso

UWBBLEAGV/AMR telemetryAI orchestration
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