A leading global retailer’s Indian operations spanned roughly 2,000 stores, each serving different customers, climates, and display capacities. But product distribution followed a one size fits all model per geography, ignoring how different each store’s actual demand really was.
The result: under inventory in some locations, over-inventory in others, and a retail network working against its own store level realities.
• Out of stock issues at stores where demand outpaced allocated inventory
• Manual, generalized distribution decisions with no store level nuance
• Low customer satisfaction when desired products simply weren’t available
• Discounts needed to clear over stocked inventory, eating into profitability
• Higher retail space required to hold excess stock that wasn’t moving
Aiwozo built an AI model to distribute inventory based on what each store actually needed, not a generalized regional assumption.
Analysis agents evaluated a range of factors per store past sales history, weather forecasts, local trends, and available display space to build a store specific demand profile.
Distribution agents used that profile to allocate more inventory to stores where products were actually selling, rather than spreading stock evenly regardless of demand.
Monitoring agents tracked sales in real time, predicting when inventory needed to be rerouted from a slower-moving store to one running low keeping distribution responsive rather than fixed at the start of a cycle.
Key business and operational challenges that limited performance, scalability, and governance before automation was introduced.
