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Industry · Logistics & Supply Chain

Data Engineering for Logistics

Multi-region lakehouses for supply chain visibility — 15 logistics systems unified on GCP, 35% forecast accuracy improvement, and real-time shipment and fleet telemetry pipelines.

Problems We Solve

Industry Challenges

Every region runs its own stack

Acquisitions and local autonomy leave each region with its own WMS, TMS, and ERP. Our GCP lakehouse unified 15 regional logistics systems into one governed, multi-region platform.

Forecasts miss because data lags

Demand planning on week-old extracts can't see the disruption happening now. Unified, fresh data improved forecast accuracy 35% in our supply chain engagement.

Customers expect live tracking

'Where is my shipment?' deserves better than yesterday's batch. We build event-streaming pipelines that fuse carrier EDI, GPS telemetry, and scan events into live shipment state.

Fleet telemetry goes unused

Vehicles stream fuel, location, and engine data that mostly lands in a vendor portal. We pipe it into your lakehouse — routing, maintenance, and emissions analytics on your terms.

Proven In Production

Measured Results

15
Logistics systems unified
one gcp lakehouse
35%
Forecast accuracy gain
demand planning
Multi-region
Resilient by design
google cloud platform
Evidence

Related Case Studies

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Questions, Answered

Logistics FAQ

Can you integrate WMS, TMS, and ERP systems across regions?
Yes — that's exactly our supply chain lakehouse engagement: 15 regional logistics systems (mixed WMS/TMS/ERP estate) unified onto a governed GCP multi-region platform with conformed shipment, order, and location dimensions.
How real-time can shipment tracking get?
Event-driven: carrier EDI messages, GPS pings, and warehouse scan events stream through Pub/Sub or Kafka into a live shipment-state model — seconds-to-minutes freshness rather than nightly batch.
Do you work with IoT and fleet telemetry?
Yes — vehicle and cold-chain sensor streams (location, fuel, temperature, engine codes) ingested at scale and joined to operational data for routing, predictive maintenance, and emissions reporting.
What did unifying data do for forecasting?
In production: 35% forecast accuracy improvement once demand planning ran on fresh, complete, consistent data across all 15 regions instead of fragmented extracts.
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