Solving On-Shelf Availability with Computer Vision
De Flow AI Team
Retail Operations
Solving On-Shelf Availability
with Computer Vision
By De Flow AI Team
The Silent Revenue Killer
When a customer reaches for a product that isn't there, you don't just lose that sale — you risk losing the customer. Studies show that after two empty-shelf experiences, shoppers begin switching stores entirely. The problem is that out-of-stocks are invisible to most systems: the POS can't sell what isn't scanned, and inventory counts often lie.
Up to 70% of out-of-stocks are caused by in-store execution — products in the back room, misplaced facings, or slow replenishment — not supplier shortages. That means most OOS is fixable on the floor, today.
👁️ How Vision-Based OSA Works
Continuous Shelf Scan
Existing cameras capture facings, gaps, and stock depth throughout the day.
Gap Detection
AI distinguishes a true empty shelf from a temporary gap or a customer reaching.
Task Dispatch
A replenishment task pings the nearest associate's device with shelf location.
📊 Manual Audits vs. Continuous Vision
| Dimension | Manual Audits | Continuous Vision |
|---|---|---|
| Frequency | 1-2x per day | Every few minutes |
| Labor cost | High (staff hours) | Near zero |
| Detection lag | Hours | Minutes |
| Consistency | Varies by associate | Always identical |
"Our top 200 SKUs are the ones that hurt most when they're empty. Now we close the gap before lunch instead of finding it at close."
— Store Operations Lead, grocery chain
Stop losing sales to empty shelves
See real-time on-shelf availability running on your existing cameras.
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