Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Transportation • 65 Vehicles

Case Study 3: Logistics Company

Marketing-optimized case study for Canadian SMBs.

$158,000
Annual Savings
385%
ROI
3.8 months
Payback
Transportation • 65 Vehicles
Industry Focus

Executive Summary

Route Optimization: ML-based dynamic routing using real-time traffic data, weather conditions, delivery time windows, and vehicle capacity constraints.

Challenge

Solution

  • Route Optimization: ML-based dynamic routing using real-time traffic data, weather conditions, delivery time windows, and vehicle capacity constraints.
  • Predictive Maintenance: IoT sensor data analysis for vehicle health monitoring with ML predictions for maintenance needs 2-3 weeks in advance.
  • Automated Dispatch: AI system assigns jobs to drivers based on location, capacity, driver qualifications, and delivery priorities.

Technical Stack

Key Metrics

18%
Fuel costs reduced 18% ($67k annual savings)
87%
On-time delivery improved from 87% to 96%
62%
Vehicle downtime reduced 62%
70%
Dispatcher workload reduced 70%
28
Average deliveries per vehicle increased from 28 to 34 daily

Implementation Timeline

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