AI-Powered Inventory Forecasting

The challenge

A retail chain with 30+ locations was over-ordering inventory by 30% every quarter due to manual demand forecasting. Buyers relied on gut instinct and basic spreadsheet averages, ignoring seasonality, local trends, and promotional impacts. The result was $2.1M in annual overstock write-downs.

The problem was compounded by inconsistent data — sales data lived in the POS system, supplier lead times were tracked in email, and promotional calendars existed as shared documents. No single system connected demand signals to purchasing decisions.

Our approach

Built a demand forecasting model using TensorFlow that incorporates historical sales data, seasonality patterns, promotional calendars, supplier lead times, and external factors like weather and local events.

Created a unified data pipeline that pulls from POS, supplier portals, and internal planning tools into a PostgreSQL warehouse, establishing a single source of truth for all demand signals.

Deployed a buyer-facing React dashboard showing per-SKU forecasts with confidence intervals, recommended order quantities, and alerts for items at risk of overstock or stockout.

Results

41% — reduction in overstock costs

$860K — annual savings in first year

92% — forecast accuracy achieved

Stack

Python, TensorFlow, React, PostgreSQL