National Retailer Runs Stores 3× Faster with IoT + AI Demand Forecasting
IoT sensor network across 400+ stores integrated with AI-powered demand forecasting — real-time inventory visibility and automated replenishment.
A 400-store national specialty retailer was bleeding $15M+ annually to stockouts and markdowns driven by manual inventory counts and a lagging forecasting system. Store associates spent up to 6 hours per shift on inventory tasks instead of customers, and category managers were working from week-old shelf-stock data.
Apptad deployed an IoT shelf-sensor network across 400+ stores, streaming events through AWS IoT Core into a Snowflake data cloud. We built an AI-powered demand-forecasting platform on top of it with Snowpark ML, then connected it to the merchandising system for automated replenishment. Store associates got a mobile app for exception-based stocking instead of full counts.
Store operations time dropped 3× — associates spend the time saved with customers. Stockouts fell 35%, markdowns from overstock dropped 22%, and the platform paid for itself in 11 months. Category managers now make decisions on data that's minutes old, not weeks.
The Numbers Behind the Story
Built On
The platforms and partners that powered this engagement. Where Apptad has a dedicated alliance, the chip links to our partnership page.
“We've stopped guessing. Our merchandisers get fresh data, our associates get their floor time back, and our customers find what they came for.”
