CLIENT
North American last-mile delivery operator
INDUSTRY
Logistics
SERVICE
AI route optimization | Intelligent dispatch allocation | Real-time ETA management | Customer communication automation | Logistics platform integration
Overview
A North American logistics operator managing a mixed fleet of vans, bikes, and electric vehicles was facing rising operating costs and declining delivery performance. The dispatch process relied heavily on manual planning, with routes created at the start of the day and rarely adjusted. This approach failed to account for live traffic, weather changes, or shifting customer priorities, resulting in missed delivery windows and reduced customer satisfaction. Partnering with Sphere, the operator implemented an AI-based route optimization and dispatch platform that improved delivery speed, cut fuel costs, and increased customer satisfaction — all within two months.
Challenges
The logistics operator’s last-mile delivery network was constrained by outdated planning processes and a lack of automation. Routes were set manually at the start of each day, with no mechanism for dynamic adjustment. Fleet inefficiencies, inaccurate ETAs, and slow responses to changing conditions were undermining performance. The most pressing problems included:
Our Solution
Sphere designed and implemented an AI-powered route optimization and dispatch engine that integrated directly with the operator’s existing logistics platform and mobile driver application. The solution combined real-time data analysis with intelligent task allocation to maximize delivery performance.
1. Real-Time Route Optimization
The AI engine ingested live traffic feeds, weather forecasts, order deadlines, and historical delivery performance data. Using advanced routing algorithms, it calculated the most efficient delivery sequences and continuously updated them throughout the day. Drivers received updated routes directly on their devices, minimizing time lost to unexpected delays and maximizing route efficiency.
2. Intelligent Dispatch Allocation
The system factored in vehicle capacity, driver experience, delivery location complexity, and current workload before assigning tasks. This ensured high-value and time-sensitive deliveries were handled by the most capable drivers, while lighter loads were distributed to smaller or more agile vehicles such as bikes or EVs.
3. Customer Communication Enhancements
The platform integrated with the operator’s customer notification system to provide accurate, real-time ETAs. If a delay was detected, the AI triggered automated alerts giving customers the option to reschedule or accept a later window. This transparency helped maintain trust even when disruptions occurred.
4. Seamless System Integration
Integration was achieved via secure APIs, with no interruption to ongoing delivery operations. The system was designed to work alongside existing dispatch workflows, enabling a smooth transition for both dispatchers and drivers. Pilot testing was completed in four weeks, followed by a full rollout in under two months.
Key Achievements
Result
The AI-based route optimization and dispatch solution gave the logistics operator the agility to adapt to real-time conditions without increasing operational complexity. By intelligently balancing fleet resources, improving ETA accuracy, and enabling dynamic route adjustments, the operator achieved significant cost savings and measurable gains in customer satisfaction — all within a rapid implementation timeline.
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Luke Suneja
Client Partner
