Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Distribution • 250 SKUs

Case Study 9: Wholesale Distributor

Marketing-optimized case study for Canadian SMBs.

$138,000
Annual Savings
420%
ROI
3.2 months
Payback
Distribution • 250 SKUs
Industry Focus

Executive Summary

Demand Forecasting: ML models predict demand by SKU using historical orders, seasonality, customer segments, and economic indicators.

Challenge

Solution

  • Demand Forecasting: ML models predict demand by SKU using historical orders, seasonality, customer segments, and economic indicators.
  • Automated Reordering: System automatically generates purchase orders when inventory reaches calculated reorder points with dynamic safety stock levels.
  • Order Processing: AI extracts order details from emails and PDFs, validates against customer history, and routes to fulfillment with 98% accuracy.

Technical Stack

Key Metrics

28%
Inventory carrying costs reduced 28% ($94k savings)
86%
Stock-outs reduced 86%
18
Order processing time reduced from 18 minutes to 2 minutes
96.8%
Order accuracy improved from 96.8% to 99.2%
1
Eliminated 1 FTE order entry role ($42k savings)

Implementation Timeline

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