Inventory Optimization Using AI in Retail
Country: United States
Industry: Retail
Products and Services: Azure AI, Inventory Insights, Power BI
At a Glance
TrendMart, a U.S.-based retail chain, was facing a familiar challenge — rising inventory costs and unpredictable product demand across regions. Their goal was to improve stock turnover and minimize costly inefficiencies.
To solve this, Think AI deployed a tailored, AI-powered solution that optimized inventory replenishment — reducing stockouts by 30% and significantly cutting overstock.
Business Need/Challenge
Their existing forecasting methods were conventional, relying deeply on static spreadsheets and historical data, offering no data that could reflect real-time consumer behavior.
This inconsistency led to wastage of stock on one hand and missed sales due to understocking on the other hand. A lack of visibility across inventory and slow decision-making further impacted profitability.
Solution
Think AI suggested the retailer to modernize its inventory approach using AI-powered intelligence. Using a combination of Azure AI, Inventory Insights, and Power BI, our team implemented a dynamic forecasting and replenishment system that could learn, adapt, and recommend optimal inventory levels. All these changes could be possible using real time data.
By integrating real-time sales, supply chain, and market data, we enabled smarter, dynamic inventory decisions across all retail touchpoints.
We integrated data details from POS systems, supply chain sources, and external market trends to chalk out a holistic inventory model. Custom dashboards in Power BI offered decision-makers clear, actionable insights at the store, category, and SKU level.
Outcome
The results spoke for themselves — a 30% reduction in stockouts, a 25% drop in overstock, and a significantly improved inventory turnover ratio. Think AI’s solution went beyond simply optimizing shelf space; it fundamentally transformed how TrendMart responds to customer demand in real time.