Tactical data is used daily and measures driver habits, fuel economy, and the overall health of the asset by tracing fault codes.

Originally appeared in Fleet Owner

Most fleet operators are overwhelmed with the amount of data that is being produced by their trucks — data comes from telematics, ECMs, fleet management systems and from many other sources. Unfortunately, all that data is in disparate data warehouses siloed within the organization and difficult to access and use for long-term fleet planning.

All this data can be used tactically and strategically. Tactical data is used daily and measures driver habits, fuel economy, and the overall health of the asset by tracing fault codes. Some onboard systems go so far as to measure and predict if a driver is at risk of having an accident.

Although tactical data can save money and enhance safety, the aggregation of tactical data combined with financial data will produce exceptionally valuable strategic data. This is where a robust business intelligence (BI) system will allow you to quickly visualize your data and understand the performance of your assets.

There are many measures of asset performance. Most of our time and effort focuses on the expense side of an asset, but to truly assess the asset value and performance we need to also determine how much revenue the asset generates.

Some organizations may look at total revenue for the fleet, but they don’t break it down on a per-asset basis. Breaking down revenue generation to the asset level will allow fleets to see that even though a truck may have higher costs, its revenue may far exceed that of other units in the fleet. This measure of productivity or ROI is critical to optimizing asset lifecycles.

Data-driven decisions are made easy by visualizing the data with a BI tool. The fleet owner sees where it makes sense to invest. Visualizing ROI data will allow for the proper allocation of valuable capital especially in times like today.

As more data is collected, a properly utilized BI system along with machine learning can begin to provide predictive analytics. A key component to predictive analytics is the ability to integrate outside market data that a company cannot control. Datasets such as interest rates, resale values, cost of fuel, and new equipment cost all play a part in maximizing ROI. Fleets need to combine market trend analysis with equipment trends to get the complete picture.

As we move into the age of vehicle electrification the ROI calculation will become more critical than ever. The cost of equipment will be much greater than that of their diesel power equivalents, the cost of damage repair will be more expensive, and parts availability may be challenging. This all leads to downtime and a less productive asset. All these items may be offset with the low cost of fuel and low maintenance costs.

The point is, without cost modeling and forecasting the ROI will be a mystery until the end of the asset’s life. The aggregation and visualization of data is critical if you want to remain competitive.