AI-Driven Logistics Optimization
Client profile
Regional logistics provider
Timeline
16 Weeks
Data Volume
5TB+ Processed
Key Outcome
Delays predicted, not reacted to
The Problem Worth Solving
An APAC logistics provider was reacting to supply chain bottlenecks rather than predicting them. Weather events, port congestion and vehicle breakdowns were driving significant delayed-delivery penalties. Their operations team held data across five systems — ERP, GPS, weather feeds, port authority APIs and a manual incident log — but no way to unify or act on it in time.
How We Thought About It
We ran a three-week data engineering discovery to assess the quality and completeness of each source before committing to any model architecture. We chose a feature-store-based approach to decouple data ingestion from model training, so models could be retrained weekly on fresh data without pipeline refactoring. We delivered a working proof-of-concept on real historical data in week six, giving the client confidence before the full production build.
What We Built
We aggregated siloed data from the ERP, GPS tracking systems and external weather and traffic APIs into a Snowflake data warehouse with a real-time streaming layer via Kafka. We trained custom PyTorch models to predict delivery delays up to 48 hours in advance, with separate model variants for weather, congestion and mechanical risk. Predictions surface through a Next.js dispatcher dashboard with one-click rerouting suggestions and a mobile alert feed for field teams.
What Changed
Dispatchers moved from reacting to disruptions to rerouting shipments before they happened. Delayed deliveries and the penalties attached to them fell materially over the first operating quarter, and optimised routing reduced fuel consumption as a second-order effect. The models are retrained weekly without engineering involvement.
“They understood our data better than we did after about three weeks. The forecast is now the first thing our planners look at each morning.”
VP of Operations
Regional logistics provider, Singapore
Tech Stack Used
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