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Logistics

Route Optimization at Scale

A $200M regional logistics company (200+ vehicles, 15,000+ daily deliveries)

$2.1M annual savings through AI-powered route optimization

KEY RESULTS

$2.1M
Annual savings: $1.1M fuel, $600K overtime reduction, $400K operational efficiency.
23%
Reduction in fuel costs through shorter routes and fewer empty miles.
93%
Delivery window compliance, up from 78%. Fewer penalties, happier customers.
40%
Fewer missed delivery windows. Customer satisfaction NPS +34 points.

Before & After

✗ Before

  • •Route planning: Static, planned night before
  • •Fuel cost per delivery: $8.40
  • •Delivery window compliance: 78%
  • •Driver overtime: 30% over budget
  • •Dispatcher hours: 6+ hours daily adjusting routes
  • •Empty miles: 18% of total

✓ After

  • •Route planning: Dynamic, updates every 15 minutes
  • •Fuel cost per delivery: $6.47 (23% reduction)
  • •Delivery window compliance: 93%
  • •Driver overtime: 8% over budget
  • •Dispatcher hours: 2 hours daily (monitoring exceptions)
  • •Empty miles: 11% of total

The Challenge

This regional logistics company operates 200+ vehicles making 15,000+ deliveries daily across a 5-state territory. Their legacy route planning system generated routes the night before—static plans that couldn't adapt to traffic, weather, or last-minute orders. By 10 AM, dispatchers were already firefighting.

PAIN POINTS

  • ×Routes planned at 8 PM were obsolete by 8 AM—traffic, weather, and late orders changed everything
  • ×Drivers missed 22% of delivery windows, damaging customer relationships and triggering penalties
  • ×Fuel costs spiraled: inefficient routes added 18% unnecessary mileage (empty miles between stops)
  • ×Dispatchers spent 6+ hours daily manually adjusting routes—reactive, not proactive
  • ×Driver overtime exceeded budget by 30%—routes didn't account for realistic drive times

Technology Stack

  • →Real-time traffic integration (HERE, Google Maps Platform)
  • →Weather-aware routing adjusting for conditions
  • →Constraint solver handling 50+ variables (capacity, time windows, driver hours)
  • →Mobile app with turn-by-turn navigation and customer notifications
  • →Dispatcher dashboard with exception alerting and manual override

Implementation Approach

1

Phase 1: Dynamic Route Engine (Weeks 1-4)

Built optimization engine that generates routes considering: real-time traffic, delivery windows, vehicle capacity, driver hours, customer priority, and fuel costs. Routes update every 15 minutes as conditions change. Initial routes are 20% more efficient than legacy system before any real-time adjustments.

2

Phase 2: Real-Time Adaptation (Weeks 5-8)

Integrated live traffic feeds and weather data. System re-optimizes routes when: traffic delays exceed threshold, weather impacts driving conditions, late orders need insertion, or drivers report issues. Drivers receive updated routes via mobile app with explanation of changes.

3

Phase 3: Predictive Capacity Planning (Weeks 9-10)

Built demand forecasting model using historical patterns, day-of-week effects, seasonal trends, and customer order patterns. Predict next-day delivery volume by zone. Enables proactive capacity positioning—right number of trucks in right locations.

4

Phase 4: Driver & Customer Experience (Weeks 11-13)

Mobile app for drivers: optimized routes, turn-by-turn navigation, delivery confirmation, exception reporting. Customer notifications: accurate ETAs updated in real-time, delivery confirmation, and proof of delivery. NPS improved 34 points.

Results Breakdown

$2.1M

Annual savings: $1.1M fuel, $600K overtime reduction, $400K operational efficiency.

23%

Reduction in fuel costs through shorter routes and fewer empty miles.

93%

Delivery window compliance, up from 78%. Fewer penalties, happier customers.

40%

Fewer missed delivery windows. Customer satisfaction NPS +34 points.

Key Learnings

  • 1.Real-time beats static every time. Even the best overnight route is wrong by morning. Continuous optimization is the unlock.
  • 2.Driver adoption is critical. We involved drivers in testing, incorporated their feedback, and gave them override authority. Adoption hit 95% by week 4.
  • 3.Start with highest-volume routes. Prove the math works before scaling to edge cases.
  • 4.Customer notifications are a feature, not an afterthought. Accurate ETAs prevent 'where's my delivery' calls.
  • 5.Dispatcher role changes from 'route maker' to 'exception handler.' Better job, better retention.

Industry Context

Amazon's AI-powered route optimization processes millions of deliveries daily with sub-hour delivery windows in major metros. Google's DeepMind estimates AI can reduce logistics emissions by up to 30% through route optimization. Early adopters see 15% cost reduction and 65% service level improvement. The competitive gap is widening.

TIMELINE

90 days from kickoff to full deployment

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