Estudio de caso · AeroespacialAnónimo por petición. Referencias nombradas bajo 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.

Fabricación · RoboticaAMR / AGV flota de inteligenciahíbrido RTLS / RFID + análisis · resultados medidos.posición de activo en vivoSuperiorAMR/AGV utilizaciónBajotiempo de carga de ocio
El desafío

Lo que estaban en contra.

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.

Nuestro enfoque

Vendor-neutral, desenlazado.

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.

Cómo lo resolvimos

Lo que entregamos.

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

El resultado que la junta vio.

Higher
AMR/AGV utilisation
Bajo
idle charging time
Más seguro
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)

Tecnología utilizada

La pila detrás de ella

UWBBLEAGV/AMR telemetryAI orchestration
¿Podría ser tu programa?

Díganos dónde está atrapado.

Treinta minutos en su caso de uso, los números, y lo que realmente mover su KPIs . Vendor-neutral, sin lanzamiento de plataforma.

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