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

                              KEY BENEFITS

                              · 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