Reducing Delivery Delays by 34%

The Problem

Cascade operated a fleet of 200+ vehicles across the Pacific Northwest. Route planning was semi-manual, demand forecasting relied on spreadsheets, and 23% of deliveries experienced delays. Fuel costs were climbing due to inefficient routing.

Our Approach

Integrated historical delivery data, real-time traffic feeds, and weather APIs into a unified intelligence layer. Used AI credits to power three modules: demand forecasting, dynamic routing, and exception handling. Each module has an independent credit budget tied to business impact.

The Solution

Built a three-layer intelligence system: (1) Demand forecasting model that predicts volume 72 hours out, (2) Dynamic routing engine that re-optimizes in real time based on traffic and weather, (3) Exception handler that automatically adjusts schedules when delays occur and notifies customers with accurate ETAs.

Results

• Delivery delays reduced by 34%
• Fleet utilization improved by 22%
• Fuel costs decreased 18%
• Customer NPS increased from 42 to 67