How Technology and Analytics Help With Asset Utilization Under Lease Agreements

by Frank Bussone

Frank Bussone is vice president of technology and data analytics for Corcentric Fleet Solutions. He has spent more than 25 years providing business analytics and business intelligence to the transportation and real estate industries. For the past nine years, he has helped Corcentric customers find lower cost solutions for their truck fleets by working through the big data broadcasted from trucks. Bussone is a certified transportation professional and received a master’s degree in urban and regional planning from Florida Atlantic University. He has also received a data science with SQL and tableau certificate from Cornell University and a certificate of achievement in six sigma green belt from Villanova University.

Frank Bussone, vice president of technology and date analytics at Corecentric Fleet Solutions talks shop on employing customized structures when setting up a lease agreement. Learn how to avoid overpaying with this insider look at utilizing analytics to their maximum potential.

When setting up a lease agreement, having customized structures for assets in different operational scenarios is pivotal. It is highly unlikely that a fleet is going to have 100% of its assets running the same amount of miles a year, especially if they are domiciled in multiple locations, running different lanes, or operating in different duty cycles. Leases need to be structured to accommodate these differences.

Once lease terms are set, the utilization of each asset needs to be studied to avoid both overutilizing assets as well as underutilizing them. Fleets want to avoid paying over-mileage penalties and additional maintenance and repair bills for overutilized assets. Conversely, they want to avoid paying for unused miles.

Over-mileage penalties are not usually significant, and it is not necessarily a bad thing to slightly overutilize an asset. However, a fleet gets into trouble if it starts running the asset outside of its most efficient life cycle.

To gain visibility into each asset, the fleet needs to share odometer readings with its lending partner. This data is found on the asset’s onboard computers, odometer readings taken during the most recent preventive maintenance or repair event, or from fuel card reports.

The next step is to compare the current utilization of each asset with its lease structure. This analysis will predict how many miles the asset will likely accumulate over the life of the lease based on the number of miles it has already been driven. This will help both the fleet and the lessor see whether the asset will be over or under its mileage as the end of the lease term nears.

Business intelligence software can be used to write the formulas once the odometer readings are obtained. By using business intelligence software, every asset can be mapped out, which will make it easy to see which assets are being overutilized and which are underutilized.

Fleets need full visibility into asset utilization to ensure they are not paying for miles that will be unused—underutilizing assets—or running trucks beyond the mileage terms of the original lease term agreement.

While mileage data needs to be collected each month, the data analysis can take place on a quarterly basis, beginning six months to a year into the lease term. Starting before a significant number of miles have been accumulated will give false projections. But waiting until the end of the lease term nears is unwise, as there will not be enough time for the fleet to adjust operations to correct the overutilization or underutilization. Those adjustments can include putting an asset in a different lane, changing location, or making other changes to performance.

Monitoring the mileage within the finance structure throughout the entire life cycle of the asset is vital because it will take the fleet time to correct the problem. A good financial partner will use data and technology to help the fleet ensure that each asset runs at the optimal utilization within its financial structure.

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