# Inventory Accuracy ML

Improved retail inventory accuracy using ML-driven anomaly detection at store scale.

## Key Takeaways:

#### Inventory inaccuracies drove lost sales

Discrepancies between system and shelf inventory reduced availability and customer satisfaction.

#### We operationalized inventory anomaly detection

Machine learning models were deployed to detect discrepancies and trigger store-level action.

#### ML-driven inventory monitoring at scale

Binomial models, anomaly detection, and classifiers were orchestrated through Databricks to send restock alerts to store associates.

#### $80M in recovered sales across 2,300 stores

Improved inventory accuracy reduced shrink and recovered lost revenue.

### Continue Reading