The carrier operated a fleet of 1,200 drivers across five metropolitan markets, delivering for three major e-commerce platforms under tight SLA commitments. Routes were planned the night before using a legacy routing tool that hadn't been updated since 2019 and couldn't account for real-time traffic, weather disruptions, or customer availability patterns.
First-attempt delivery success was running at 84.2% — meaning 15.8% of packages required a second or third attempt. Each failed delivery cost the carrier $7.40 in re-route labor, fuel, and customer service overhead, adding up to $4.4M in annual waste. The legacy system also created chronically unbalanced routes: some drivers were finishing their stops by 2pm while others were running past 9pm, driving burnout and a 67% annualized driver turnover rate.
The operations team had no real-time visibility into fleet status. Dispatch coordinators managed exceptions via group text messages and phone calls, creating a chaotic communication layer that consumed 34 FTEs worth of coordination overhead.
Our dispatchers were managing 1,200 drivers via group texts and gut instinct. Every morning we sent drivers out with routes that were already obsolete by 10am. We were running a modern delivery operation with logistics technology from the last decade.
Intelliblitz deployed a three-engine intelligent fleet command system. Engine one: dynamic route optimization using real-time traffic data, historical delivery success patterns, and customer availability scoring. The system re-optimized routes continuously throughout the day — not just at dispatch — redistributing stops across nearby drivers when delays, cancellations, or new pickups occurred. Route balance variance dropped from 3.4 hours between shortest and longest routes to under 45 minutes.
Engine two: a predictive delivery success model. The AI analyzed 42 signals per stop — including time-of-day delivery history at each address, building access patterns, weather conditions, and package type — to predict first-attempt success probability. Stops with low success probability were automatically rescheduled to optimal time windows or flagged for customer pre-notification, dramatically reducing failed attempts.
Engine three: a unified fleet command dashboard replacing the group-text dispatch chaos. Real-time GPS tracking, automated exception handling, and AI-powered dispatch recommendations enabled 8 coordinators to manage what previously required 34 — with better outcomes and faster response times.
The system detected that deliveries to apartment complexes in our Houston market had a 31% failure rate between 11am-2pm because residents were at work. It automatically shifted those stops to 5-7pm windows and our success rate at those addresses jumped to 94% overnight.
— Head of Route Planning, Regional Last-Mile Carrier
Within 90 days, first-attempt delivery success climbed from 84.2% to 96.8%, eliminating $3.8M in annual re-delivery costs. Cost-per-delivery dropped 28% as route optimization reduced average daily mileage per driver by 23 miles and balanced workloads eliminated chronic overtime. Fuel costs fell $1.7M annually.
Driver turnover dropped from 67% to 37% annualized as balanced routes and predictable schedules addressed the primary driver of attrition. Dispatch coordination headcount went from 34 to 8 FTEs — the remaining coordinators handled strategic exception management rather than manual route tracking. The carrier's improved SLA performance triggered a contract expansion with its largest e-commerce client, adding 18,000 daily deliveries and $22M in annual revenue. Two competing carriers approached for partnership discussions after seeing the fleet intelligence capabilities in operation.
Our largest client told us we went from being on their vendor watchlist to being their preferred carrier in 90 days. The fleet command system didn't just fix our operations — it saved our most important customer relationship and then grew it by 40%.
— CEO, Regional Last-Mile Carrier