AI Replenishment Models Lift Retail Margins
FLO deployed AI-driven replenishment algorithms that raised on-shelf availability from 71 percent to 94 percent and cut out-of-stocks from 15 percent to 3 percent. The system produced a 2.7 percent revenue gain while operating inside already thin retail margins.
This case shows how targeted forecasting replaces manual guesswork with quantified demand signals. Teams stop reacting to empty shelves and start managing inventory by measurable probabilities.
FLO implemented the models and recorded the reported availability and revenue lifts according to Product School case data.
Step 1: Connect your sales and inventory CSV files to an AI forecasting platform such as Akkio at akkio.com. Step 2: Select the replenishment prediction model and train it on the last twelve months of daily stock movement. Step 3: Export the daily order recommendations and load them into your ERP; expect the first measurable reduction in stock-outs within two weeks.