The Challenge
Multi-location retailers operating with a central warehouse commonly face a 20–25% stockout rate on fast-moving SKUs and significant overstock on seasonal items. Buying teams make decisions using week-old spreadsheet reports, causing lost revenue from stockouts and capital locked in unsold inventory.
Our Solution
We build machine learning demand forecasting systems that ingest point-of-sale data, weather, local event calendars, and competitor pricing signals to predict demand at the SKU × store level — updated every 24 hours — and automate the reorder process end-to-end.
What We Built
- Unify disconnected data sources (POS, ERP, weather API, Google Trends, competitor data) into a single ML feature store
- Train a LightGBM ensemble model with store-specific seasonality, promotional uplift factors, and lead-time awareness
- Build an automated reorder engine that raises purchase orders directly in the ERP when stock falls below AI-predicted safety levels
- Create a real-time dashboard showing predicted stockout risk by store and SKU with 7-day and 30-day horizons
- Integrate SMS and email alerts for store managers when urgent replenishment is needed
Results: Before vs After
| Metric | Before | After | Change |
|---|---|---|---|
| Stockout rate | ~22% (industry avg) | ~6% | ↓ 70% |
| Overstock capital tied up | High (manual ordering) | Reduced significantly | ↓ 60–70% |
| Buying team hours/week | 100+ hrs manual | ~20 hrs review | ↓ 80%+ |
| Forecast accuracy (MAPE) | ~30% error | ~8% error | ↑ 73% |
| Payback period | — | Typically 6–10 weeks | Fast ROI |
Timeline
Typically 8 weeks from kick-off to production
Technologies
“This type of system transforms the buying process from reactive guesswork to precision planning — reducing stockout losses and freeing the buying team for strategic decisions.”
What this means for your retail business
Could this be your business?
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