Detect unnoticed out-of-stock inventory in stores

Target Audience

Store operations teams, inventory managers

Challenge

Physical audits revealed that half of Target's out-of-stocks were unknown to their inventory systems — meaning systems showed items as available when shelves were actually empty. Traditional tracking couldn't detect these discrepancies caused by shipping mistakes, theft, and misplaced items. With over 100,000 SKUs across nearly 2,000 stores, manual detection was impossible.

Solution Approach

Target created the Inventory Ledger to track every inventory change in real-time. They developed an ensemble of AI models, each specialized for different product categories, trained on millions of labeled examples of unknown out-of-stocks. The system automatically detects patterns indicating inventory inaccuracies, then triggers replenishment.

Value Add

Target reports 'substantial sales lift' for products that would otherwise have been unavailable. The AI eliminated the need for costly shelf-edge cameras and sensors that had proven difficult to maintain at scale.

How to Apply This

Out-of-stock cases in your inventory can hardly be tackled with off-the-shelf AI solutions. Likely, you will need to leverage your unique data, just as was the case for Target. A data project for such scenarios takes some time to dive deep into your individual data situation - but can pay off with high-quality predictions that allow you to take actions earlier. A main question to get started with an own solution is: What are sources of information from which you can identify that your stock information is wrong? And how do you notice such situations today?

Need guidance to get started or drive your data analysis for your inventory management? Reach out to me and book a free 30-minute feasibility call.