How we sped up supply chain restocks by 10x using demand forecasting

Inventory managers were using massive, disconnected Excel sheets to track stock across 15 warehouses. This led to frequent out-of-stock events for high-demand items and overstocking of slow movers.

We engineered an intelligent data pipeline and demand forecasting tool using Python, FastAPI, and pgvector. The system analyzed historical sales logs and live distributor stock data to automatically trigger replenishment forms exactly when needed, entirely replacing the manual spreadsheet workflow.

The automated system sped up the restock decision process by 10x. It prevented major out-of-stock events during holiday seasons, increasing overall sales volume and minimizing warehouse holding costs.