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.

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