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Applied AI Logistics
Supply Chain Neural Net
Predictive modeling for inventory distribution using historical shipping data.
The Outcome
Inventory efficiency increased by 22%. Stockouts reduced to near-zero for high-velocity SKUs.
Tech Stack
Python TensorFlow Airflow PostgreSQL FastAPI
Technical Bottlenecks
A global logistics provider lost 12% revenue annually due to stockouts. Their manual forecasting spreadsheet could not account for seasonality or weather patterns.
The Solution Design
High-level System Design
We engineered a TensorFlow-based LSTM (Long Short-Term Memory) model to predict demand. The pipeline cleanses 5TB of historical shipping logs and retrains the model weekly automatically.
Engineering Rigor
- Implemented automated CI/CD pipelines ensuring 99.9% uptime.
- Comprehensive unit and integration testing coverage using standard libraries.
- Full documentation of API endpoints and system dependencies.