Demand Forecasting Across 300+ Locations
ML-driven forecasting cut food waste and improved staffing accuracy chain-wide.
The Problem
A fast-casual chain with 300+ locations relied on manager intuition for ordering and scheduling, resulting in inconsistent food waste and both over- and under-staffing across the portfolio. Corporate had no reliable way to see which locations were over-ordering versus running lean.
The Architecture
POS, weather, and local event data were unified into a single Databricks pipeline. A forecasting model trained per-location-cluster runs nightly on Databricks jobs, publishing next-day demand curves to a lightweight internal ordering and scheduling app via a governed Gold-layer API.
The Solution
Location managers receive daily prep and staffing recommendations generated from the forecast, with the ability to override based on local knowledge — overrides feed back into model retraining, so the system improves the more it's used.
Business Value
Food waste dropped 28% chain-wide within the first quarter, and the platform reached full rollout in four weeks from kickoff. Labor scheduling accuracy improved enough that several regions reduced overtime spend materially.
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