AI Inventory Optimization for Retail
AI-driven demand forecasting that reduced stockouts by 50% and cut excess inventory costs by $1.5M annually.
50%
Fewer Stockouts
$1.5M
Annual Savings
92%
Forecast Accuracy
The Challenge
A retail chain with 200+ locations struggled with inventory imbalance — popular items frequently sold out while slow movers occupied valuable shelf space. Manual demand forecasting based on historical averages failed to account for seasonality, local events, weather patterns, and promotional impacts.
Our Solution
We built an AI demand forecasting engine that processes point-of-sale data, weather forecasts, local events, promotional calendars, and competitor pricing to generate store-level demand predictions. The system produces automated reorder recommendations, identifies transfer opportunities between locations, and provides markdown timing suggestions for slow-moving inventory.
Technologies used
Results
- Stockout incidents reduced by 50% across all locations
- Excess inventory carrying costs cut by $1.5M annually
- Demand forecast accuracy improved from 65% to 92%
- Markdown losses reduced by 30%
More Case Studies
Explore other AI projects we have delivered across different industries.
Have a Similar Challenge?
Tell us about your business problem. We will show you how AI can solve it with a proven approach.
No commitment required · 30-minute call · Actionable insights guaranteed