A smart alerting solution for one of the largest FMCGs through identifying OOS and Off-Sales anomaly

CASE STUDY

A smart alerting solution for one of the largest FMCGs through identifying OOS and Off-Sales anomaly

SUMMARY

Client is one of the largest (Fortune 500) FMCG companies in the world. Client wanted to develop a system to generate smart alerts for merchandising teams at a store level based on off-sales trend

· Identifying OOS Situation: Built ML-based models to identify the Out-Of-Stock scenario in a store at an SKU level taking into account the level of phantom inventory

· Identifying Off-Sales Behavior: Built ML-based models to identify the off-sale behavior of an SKU in a particular store attributable to phantom inventory, stock less than presentation stock or improper operations within the store

· Smart Alerts: Built a system that generates alerts for the store manager and merchandising teams to maintain healthy stock in the store and increase the revenue

APPROACH

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

RESULTS

· 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

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