Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Food Service • Wholesale + 2 Retail Locations

Case Study 57: Artisan Bakery

Marketing-optimized case study for Canadian SMBs.

$72,000
Annual Savings
475%
ROI
2.6 months
Payback
Food Service • Wholesale + 2 Retail Locations
Industry Focus

Executive Summary

Demand Forecasting: ML predicts daily demand by product based on historical sales, weather, events, and day-of-week patterns. Optimizes production batches to minimize waste.

Challenge

Solution

  • Demand Forecasting: ML predicts daily demand by product based on historical sales, weather, events, and day-of-week patterns. Optimizes production batches to minimize waste.
  • Wholesale Ordering: Automated order management for restaurant and cafe accounts. Predicts reorder timing and generates order suggestions based on usage patterns.
  • Recipe Scaling: AI automatically scales recipes based on production needs, adjusts for batch sizes, and generates ingredient pull lists for bakers.

Technical Stack

Key Metrics

18%
End-of-day waste reduced from 18% to 6% of production
72%
Stockouts reduced 72% (better availability)
84%
Wholesale order processing time reduced 84%
Improved
Baker efficiency improved (production planning automated)
$48
Food waste reduction saved $48k annually + labor savings

Implementation Timeline

Week 1-3

Historical sales data analysis and model training

Week 4-5

Recipe database setup and scaling automation

Week 6-7

Wholesale portal development

Week 8

Testing and baker training

Week 9

Production rollout

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