Inventory that predicts demand before you do
LogiTech Distribution — 30 locations, 12,000 SKUs
Timeline
10 weeks
Technologies
Python, XGBoost, PostgreSQL
45%
Less inventory waste
92%
Forecast accuracy
3.2×
Faster replenishment
$28K
Monthly savings
The Challenge
LogiTech was losing $180K annually to expired stock and stockouts. Their team spent 20+ hours weekly on manual replenishment spreadsheets across 30 locations, with no unified view of inventory health.
Our Solution
We built an ML-powered inventory intelligence platform integrating their POS, ERP, and supplier APIs. The system forecasts demand per SKU per location, automates purchase orders, and provides real-time dashboards for operations managers.
What we built
ML demand forecasting by SKU, location, and season
Automated purchase order generation with supplier integration
Real-time stockout and overstock alerts via Slack and email
Per-location performance dashboards with drill-down
ABC analysis and dead stock identification
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