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