Logistics · AI / ML
Real-Time Fleet Optimization Across 12 Nigerian States
A national logistics company needed to optimize routes across Nigeria's complex road network. Our Vertex AI model dynamically reroutes fleets across 12 states.
The Challenge
The client operated a fleet of 200+ vehicles across 12 Nigerian states. Route planning was done manually, ignoring real-time traffic conditions, fuel price variations between states, and security data. This led to delayed deliveries, excessive fuel costs, and driver safety concerns.
Our Solution
We built a real-time fleet optimization platform on Google Cloud using Vertex AI. The model ingests live traffic feeds, fuel price APIs, weather data, and crowdsourced security reports to calculate optimal routes. Dispatchers receive dynamic route updates, and drivers get turn-by-turn guidance through a mobile app.
The Results
The platform delivered a 4.2x return on investment within six months. Fuel costs dropped by 31%, on-time delivery rates improved to 94%, and the operations team reduced route planning time from hours to minutes.
4.2x
ROI in 6 months
31%
Fuel cost reduction
94%
On-time delivery rate
Business Context
The organization managed fleet operations across multiple operating environments with variable traffic, route reliability, and dispatch complexity. Manual route planning created high planning overhead and inconsistent execution quality.
Leadership needed a repeatable decision framework that improved dispatch quality while giving operations teams clear visibility into performance changes.
Implementation Approach
We developed a routing intelligence layer combining real-time signals with operational constraints. Dispatch teams received ranked route options with contextual risk indicators rather than static path suggestions.
The rollout used staged deployment by corridor and operations unit, allowing teams to validate behavior and adoption before expanding scope.
Operational Outcomes
Planning quality improved because route decisions reflected current conditions and business constraints in the same operating view.
Operational predictability improved as dispatch choices became less dependent on manual heuristics and more grounded in shared decision logic.
Governance and Compliance
We embedded operational governance through event logging and decision transparency, supporting leadership review and continuous process refinement.
The architecture supported controlled iteration so model and rules changes could be introduced without destabilizing field operations.
Case Study FAQ
How do dispatch teams adopt AI route recommendations?
Adoption improves when recommendations are transparent, explainable, and integrated into existing dispatch workflows instead of replacing them abruptly.
Can this work in low-connectivity routes?
Yes. We use resilient sync and fallback logic so teams can continue operating when connectivity quality changes.
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