Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Food Service • 8 Locations

Case Study 5: Restaurant Chain

Marketing-optimized case study for Canadian SMBs.

$94,000
Annual Savings
465%
ROI
2.6 months
Payback
Food Service • 8 Locations
Industry Focus

Executive Summary

Inventory Forecasting: ML models predict demand by location, day of week, weather, local events. Automated ordering system places supplier orders 3 days in advance.

Challenge

Solution

  • Inventory Forecasting: ML models predict demand by location, day of week, weather, local events. Automated ordering system places supplier orders 3 days in advance.
  • Labor Scheduling: AI optimizes staff schedules based on predicted foot traffic, employee availability, labor cost constraints, and skill requirements.
  • Waste Tracking: IoT scales and image recognition track food waste by category with recommendations for portion adjustments.

Technical Stack

Key Metrics

$187
Food waste reduced from $187k to $123k annually
$28
Labor costs optimized saving $28k annually
81%
Stock-outs reduced 81%
12
Manager time spent on scheduling reduced from 12 hours to 2 hours weekly

Implementation Timeline

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