AI-Powered Demand Forecasting Model for a Multi-Location Bakery Chain

Overview

Built a demand forecasting model for a multi-location bakery chain that predicts how much of each product to bake per location per day, based on historical sales, day of week, and seasonal patterns. Store managers had previously been estimating production quantities by feel, which led to both stockouts of popular items and significant unsold waste on slower days. The model's daily recommendations are delivered directly into the production planning sheet managers already use each morning, requiring no new software to learn. We validated the model against a full year of historical sales data across all locations before rolling it out chain-wide. Store managers at a multi-location bakery chain were estimating production quantities by feel, leading to both stockouts of popular items and significant unsold waste on slower days. We built a demand forecasting model predicting how much of each product to bake per location per day, based on historical sales, day of week, and seasonal patterns, delivering recommendations directly into the production planning sheet managers already use. We validated the model against a full year of historical sales data across all locations before rolling it out, comparing its recommendations against what managers had actually baked and sold historically. Managers used it alongside their own judgment for a few weeks before relying on it as the primary planning input. Total unsold product waste across locations dropped by an estimated 18%, while mid-day stockouts of the chain's best-selling items also fell — improving on both fronts rather than trading one for the other.