The approach to address the client’s challenge included:

· Analyze Store-SKU behavior and estimate phantom inventory

· Calculate corrected inventory at the store to estimate reorder point

· Generate OOS and zero scan alerts based on inventory levels and sales patterns at the store

· Use advanced ML algorithms to forecast Store-SKU level sales and compare with actual sales to identify anomaly due to shelf mismanagement

· Prioritize alerts based on business rules and $ opportunity


· ML-driven model to evaluate the impact of trade promotion spends

· Scalable platform to understand the trade spend effectiveness across brands and regions

· Visualization platform cum scenario planner was embedded to help category managers optimize trade spends


· Acting on 3% OOS results in an overall revenue boost of 4%

· Nudging merchandising teams to achieve higher alert reach resulted in an additional 1.5% revenue