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.
What they were up against.
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.
Client identity withheld under confidentiality. Figures reflect outcomes from comparable RTLS/RFID deployments; your own numbers depend on use case, environment and execution.
Vendor-neutral, outcome-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.
What we delivered.
- UWB RTLS real-time robotic positioning
- BLE asset-interaction tracking
- AGV/AMR telemetry route analytics
- AI fleet orchestration — congestion, idle, predictive optimisation
The outcome the board saw.
“We stopped managing robots as individual machines and started optimising them as an intelligent fleet.”
— Director of operations, industrial manufacturing (client anonymised)
The stack behind it
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