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Analytics · Freight Forwarders

Container utilization and consolidation analytics

A forwarder analyzed container fill and consolidation opportunities to move the same goods in fewer boxes.

Microsoft FabricPower BIAzure Data FactoryDAX measures
Fill rate
+12 pts
illustrative
Consolidation
Identified
pre-booking
Cost per unit
Lower
per shipment
Lanes covered
All
major

Illustrative scenario · example figures

The challenge

Containers shipped under-filled because booking decisions were made in isolation, and no one could see where orders could have been consolidated.

What we built

We modeled volume and weight utilization per container and identified consolidation opportunities by origin, destination, and time window.

  • Fill rate by volume and weight per container and lane.
  • Consolidation opportunities by origin, destination, and window.
  • Trend on utilization against target.

Outcomes

  • Under-filled containers are visible and reduced.
  • Consolidation opportunities are acted on before booking.
  • Freight cost per unit shipped falls.

Where the data came from

TMSBooking recordsOrder dataCarrier EDI

Data is drawn from the systems the operation already runs on and delivered in the client’s own tenant.

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