Rising fuel prices and increasingly strict sustainability targets were putting pressure on fleet-heavy operations. One enterprise set out to find a data-driven way to reduce fuel waste, improve driving efficiency, and lower its environmental impact.
Fuel consumption was becoming a major cost driver across daily operations.
New environmental benchmarks demanded measurable efficiency gains.
A controlled pilot ensured results could be proven before scaling.
The organization faced multiple challenges common to large fleets:
They needed a solution that could deliver measurable savings while supporting long-term environmental goals.
Rising fuel costs with limited visibility into waste.
Inefficient driving behaviors such as excessive idling and harsh acceleration.
Route inefficiencies impacting fuel consumption.
Growing pressure to meet sustainability and emissions targets.
Lack of real-time insights to drive corrective action.
Core9 deployed DriveSense, its AI-powered fleet monitoring system, to deliver real-time visibility into fleet performance.
Drivers and fleet managers received clear, actionable insights, showing exactly where fuel was being wasted and where behavior changes could improve efficiency. This enabled immediate intervention and ongoing optimization across the fleet.
Core9 deployed DriveSense, its AI-powered fleet monitoring system, to deliver real-time visibility into fleet performance.
Drivers and fleet managers received clear, actionable insights, showing exactly where fuel was being wasted and where behavior changes could improve efficiency. This enabled immediate intervention and ongoing optimization across the fleet.
The results were both immediate and quantifiable.
The pilot demonstrated that real-time monitoring could quickly translate insight into impact.
Approximately 10,000 liters of fuel saved during the pilot.
3 percent reduction in total fuel consumption across 200 vehicles.
26 tons of CO₂ emissions reduced per month.
Improved driving behavior and operational efficiency across the fleet.
By combining AI-driven insights with continuous monitoring, the fleet achieved:
Lower fuel costs and reduced operational waste.
Tangible progress toward sustainability targets.
Improved accountability and visibility for fleet managers.
A scalable foundation for long-term efficiency gains.
The initiative proved that cost savings and environmental responsibility can be achieved simultaneously with the right technology.
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