AI & ML

Supply Chain Optimization

Graph-based route optimization for international shipping — reduced fuel consumption by 14% annually.

14%

Fuel Cut

98.4%

On-Time Arrival

18,000t

CO2 Reduced

The Challenge

An international maritime shipping company struggled with sub-optimal vessel routing, resulting in excessive fuel burn and port delay surcharges.

The Solution

We developed a graph neural network model with PyTorch and Neo4j that analyzes ocean currents, weather patterns, and port congestion to recommend fuel-efficient routes.

Tech Stack

PyTorchNeo4jAzure

Key Features

Graph Neural Network
IoT Sensor Stream
Dynamic Route Optimization

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