Details anonymised at client request.
Business Situation
The company relied heavily on manual replenishment planning across stores, resulting in recurring stockouts during peak-demand periods and inconsistent inventory decisions across locations. Leadership had visibility into rising stockout-related losses and emergency restocking activity, but lacked a reliable forecasting mechanism that could improve replenishment timing and inventory planning across the retail network.
What the Consultant Did
An independent AI and operations consultant developed a predictive replenishment model using historical store-level sales and inventory data across the retail network. The engagement included demand forecasting, replenishment optimisation, ERP integration planning, operational workflow redesign, and implementation of store-level replenishment recommendation systems. Particular focus was placed on creating a practical operating model that combined forecasting automation with manager oversight rather than replacing store-level decision-making entirely.
What Changed
The revised replenishment process improved inventory visibility, reduced emergency restocking frequency, and lowered stockout rates across high-volume SKUs. The company also established a more structured replenishment workflow that improved coordination between store operations and inventory planning teams.
Evidence, not adjectives.
The measurable changes recorded during or following the engagement.
Stockouts on top-selling SKUs reduced from 14% to 9.3%
Emergency restock orders reduced by 54%
Estimated annual operational savings of approximately ₹91 Lakh
Forecast-driven replenishment process implemented across stores
