Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Retail • 11 Stores

Case Study 27: Grocery Chain

Marketing-optimized case study for Canadian SMBs.

$243,000
Annual Savings
365%
ROI
4.1 months
Payback
Retail • 11 Stores
Industry Focus

Executive Summary

Demand Forecasting: ML predicts product demand by SKU, location, day, weather, holidays, and local events. Automated ordering reduces overstock and stockouts.

Challenge

Solution

  • Demand Forecasting: ML predicts product demand by SKU, location, day, weather, holidays, and local events. Automated ordering reduces overstock and stockouts.
  • Shrinkage Detection: Computer vision at self-checkout identifies potential theft, produce mis-scanning, and checkout errors. Alerts staff to intervention opportunities.
  • Labor Optimization: AI schedules staff based on predicted traffic patterns, historical transaction data, and employee availability.

Technical Stack

Key Metrics

$178
Food waste reduced $178k annually through better forecasting
2.8%
Shrinkage reduced from 2.8% to 1.4% ($142k savings)
67%
Stock-outs reduced 67%
$97
Labor costs optimized saving $97k annually
18%
Inventory carrying costs reduced 18%

Implementation Timeline

16 weeks for full rollout across all locations

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