AI USE CASE
Inventory Shrinkage Root Cause Analysis
Help retail ops teams pinpoint exactly why inventory is disappearing and where.
What it is
Machine learning models cross-reference inventory discrepancies, delivery records, and point-of-sale patterns to automatically surface the most likely causes of shrinkage, whether theft, supplier fraud, scanning errors, or process failures. Investigations that previously took weeks can be triaged in hours, and loss prevention teams can prioritise store visits with the highest expected ROI.
Data you need
Historical inventory counts, goods-in delivery records, SKU-level POS transaction data, and ideally staff scheduling data spanning at least 12 months.
Required systems
- erp
- ecommerce platform
Why it works
- Clean, consistent inventory and delivery data across all stores before model training begins.
- Loss prevention managers involved early to validate output categories and build trust.
- Clear escalation workflow tied to model alerts so findings translate into store visits.
- Regular model retraining as shrinkage patterns shift seasonally or by store format.
How this goes wrong
- Inventory data is too inaccurate or inconsistently recorded to train a reliable model.
- Loss prevention teams distrust model outputs and revert to gut-feel investigations.
- Shrinkage categories are too granular or store processes too heterogeneous across locations for a single model to generalise.
- Model flags high-risk stores but no workflow exists to act on the prioritisation.
When NOT to do this
Don't deploy this if your stores still rely on manual cycle counts entered in spreadsheets, the data quality will make model outputs unreliable and erode stakeholder trust from day one.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.