Don't let rising fuel prices burn a hole in your fleet budget
April 04, 2022
Rising fuel prices pose significant, unplanned cost implications for your fleet. Idling causes fuel burn that can further impact your bottom line. By leveraging telematics data, you can forecast the cost of wasteful idling and make data-driven decisions to reduce fuel costs.
Reduce idling costs
What the data shows
In this scenario, a service fleet using idling durations and estimated costs telematics data experienced an average 20% idling rate compared to engine hours on a previous 12-month run rate. This equates to 10.5 hours of idling and an estimated cost of $17.60 per vehicle, per month.
Forecasting with telematics data
Based on U.S. fuel price average of $4.17* per gallon and using telematics historical trends and actual data, a “do nothing” approach for this fleet will result in an additional nearly $6.00 per vehicle, per month due to idling alone, increasing the total estimated cost to $23.50 per vehicle, per month for allowed idling.
Savings from reduced idling allowance
From this forecasted telematics data, you can make informed decisions for your fleet. The table below demonstrates how this fleet could reduce idling allowance and save hundreds or thousands per month.
| Reduction | Baseline cost (do nothing) | Estimaged adjusted cost | Estimated avoidance cost |
|
15% |
$23.5 |
$20.0 |
$3.5 |
|
25% |
$23.5 |
$17.7 |
$5.9 |
|
50% |
$23.5 |
$11.8 |
$11.8 |
Cost avoidance estimations on a per vehicle, per month basis.
Monitor idling and report on trends at a vehicle level across all our available solutions today. With our GEOTAB solution, one of Element’s pure-play telematics providers, fleet managers can also enable in-cab buzzers when drivers are idling over the allowed threshold. This encourages self-correcting fleet driver behaviors while providing fleet managers efficient reports to monitor and action on non-compliance.
*Fuel price average as of March 8, 2022.
Learn more about how you can prevent fuel fraud with Fuel EKG, the first artificial intelligence-powered fuel modeling solution.
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