AI Cuts Food Waste by 25% for Durham Bakery
The Problem
A family-run bakery in Whitby had a consistent over-production problem. Without any forecasting system, the head baker relied on gut feel to decide how much to produce each day — and was routinely writing off $600–$800/week in unsold croissants, loaves, and pastries. Margins were already thin, and this waste was quietly eating the bakery's profit.
Key Pain Points
- $600–$800 in unsold product written off every week
- No system to account for weather, events, or seasonal patterns
- Over-ordering on ingredients led to spoilage beyond just finished goods
- Owner spending time manually adjusting orders with no data backing
Our Approach
We implemented an AI demand forecasting model trained on 18 months of their POS sales data, layered with local event calendars, school schedules, and daily weather feeds. The system generates a production plan each morning, item-by-item, and sends it directly to the baker's phone before they start prep.
Sales Data Audit
Pulled and cleaned 18 months of POS history, tagging by SKU, day-of-week, season, and weather conditions.
Forecasting Model Build
Trained a lightweight ML model to predict daily demand per product — factoring in local events, school holidays, and weather.
Automated Morning Briefing
Set up a daily SMS + email alert that delivers the production plan to the owner each morning at 5 AM before prep begins.
Feedback Loop
A simple end-of-day check-in (2 taps on phone) feeds actual sell-through back into the model so it keeps improving.
Results Dashboard
Daily Production Accuracy — Before vs. After
Key Outcomes
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