One of the key responsibilities of a fleet manager is to minimize the capital and operating costs of the fleet they manage. An important tool to accomplish that is life-cycle cost analysis. Effective life-cycle cost analysis can determine how long assets can remain in the fleet before the cost of ownership and operation are no longer cost-effective. The goal is to spread capital costs over as long a period as possible before the ongoing total cost of ownership begins to rise year over year. The analysis requires that fleets accurately classify assets; collect maintenance, fuel, and downtime costs to model annual operating costs; and capture purchase and disposal costs to model the cost of ownership.
While this is a critical skill, many fleets don’t conduct life-cycle analyses because they don’t have the necessary data to conduct the analysis, and doing the analysis requires time to collect data, transform it, set up the model, and run the analysis. The process can be easier if fleet managers take steps to ensure that the needed data is captured and properly organized in their fleet management system.
Set Up Life-Cycle Categories
The first step is to organize your fleet assets into categories of like assets. Life-cycle analysis is most effective when it is comparing categories of assets that have similar configurations, acquisition costs, and operating use cases. A basic mid-size sedan could be set up as an administrative vehicle or outfitted as a marked police car. The configurations and costs will be different because of the upfitting to the police car, and how these two vehicles are used will be extremely different, resulting in different annual operating costs. These differences are enough that a police vehicle may have a life cycle of four to six years, where an administrative sedan may have seven to nine years.
Life-cycle categories are used to select and group records from the fleet information system to calculate average monthly operating and capital costs. Depending on the fleet system, these may be an existing code or field on the asset record or may be a user-defined field assigned to the asset.
When setting up life-cycle categories, consider a few factors:
- The more defined the category, the fewer the assets in each category, which can make the sample set statistically too small to analyze.
- Very broad categories (e.g., sedans or heavy trucks) will combine assets that have different capital and operating costs, making the analysis results too general to establish accurate life cycles.
- The more categories, the more models that must be constructed and calculated.
The American Public Works Association’s (APWA) equipment code is growing in popularity among government fleets as a tool to classify fleet assets. The APWA code is a string of up to 10 characters that groups assets together that have the same class, unit type, fuel, weight, application, power source, wheel configuration, transmission, and a user-defined field.
Some fleets have implemented the full 10-character set, and others use a portion of the code set to classify their fleet. Each position has an alpha-numeric character that is assigned to a standard attribute defined in the code set, making it easy to code an asset based on its configuration. The first four positions that form the general class, unit type, and energy source create enough differentiation among assets to do effective analysis, without generating too many categories to analyze. For example, an A32G is a gas-powered intermediate four-door sedan.
Whether you adopt an existing internal code or recode the fleet, each asset should be assigned a class code. Before running a new life-cycle analysis, make sure that assets are correctly assigned to the right class. Look for assets with the same make and model that have different class codes.
Using a VIN decode tool is another good method for standardizing asset data and identifying if assets are correctly grouped.
Many fleets don’t conduct life-cycle analyses because they don’t have the necessary data to conduct the analysis, and doing the analysis requires time to collect data, transform it, set up the model, and run the analysis.
Input Ownership Cost Data
Cost of ownership is a key input for life-cycle analysis. For each period (month or year, depending on the analysis) an asset is in service, the net difference in residual value from one period to another is the asset’s period capital cost. Residual value is generally the market value of the asset if it were sold in that period.
Fleets are generally not in the practice of selling operating assets in good condition early in their life, so the actual residual value in those early years is not data their fleet information system would typically have. A market valuation guide or service can be used to construct a residual cost curve, but these typically require a subscription and specific data like an asset’s configuration, mileage, condition, and locations to calculate the values. At the end of each period, each asset’s market value could be determined and used to calculate an average value for each period.
An easier method is to use depreciation to estimate the period’s residual value. The sum of years or double declining methods is the best for modeling residual values, as they decline in value sharply in the early years and flatten out in later years, which is like actual asset values on the market.
When analyzing life cycles, it doesn’t matter which calendar month a repair or fuel transaction was made in, but rather which month in the asset’s life it happened in and what utilization was at that time.
This method should not be confused with the financial depreciation used to calculate the financial value of an asset for financial statements. The method and terms used may not align with the actual life-cycle of the assets. Straight-line depreciation is not a good method for life-cycle analysis as it assumes that the capital cost is the same in every period, which means that the life-cycle analysis is only based on operating costs because the net change in capital costs period to period is zero. The practice may also be to depreciate the asset over eight years but sell it after 12.
The input to these calculations includes the total capital value of the asset, the expected salvage value as a percent of the acquisition value, and the number of months in service. This information should be recorded in the fleet information system.
The total capital value should include the purchase price, plus the cost of upfitting the asset. When assets are sold, the salvage revenue net of any disposal costs should be recorded, along with the mileage and age of the asset when it is taken out-of-service. The average age and mileage when taken out of service is used to calculate the life-cycle depreciation term. The average of the total capital cost divided by the salvage revenue is the average salvage percentage.
Excel has functions that can be used to calculate the period’s depreciation expense. The first period’s expense is subtracted from the original purchase price to get the capital cost in that period. From there on, the expense in each period is subtracted from the cost in the prior period until the final period is the same as the term used in the calculation.
Add Maintenance Cost Data
When analyzing life cycles, it doesn’t matter which calendar month a repair or fuel transaction was made in, but rather which month in the asset’s life it happened in and what utilization was at that time. Life-cycle analysis aggregates the monthly maintenance data into each service month for the set of assets in the analysis. This means that costs in month nine could be costs that happened this year in a new vehicle, or in month nine of a nine-year-old vehicle purchased in 2010.
The transformation is done by subtracting the in-service month from the month that the work was done and assigning that age to the repair data. New vehicles will only have costs for the few months in service, where old assets will have costs for several years. In some months, assets will have zero maintenance costs, which is fine because this is combined with the data from assets that did have maintenance to calculate what the average expense is for that service month.
Therefore, it is important to have accurate in-service dates recorded on the assets and to properly date the work order so the correct age can be calculated. Costs should only be recorded from the time the asset is in-service until it is taken out of service. It should not include zero costs in the months it is waiting to be disposed of. Also, recording accurate meter readings on the work order is essential to correlate the repairs to the life-to-date usage of the asset.
Maintenance costs make up most of the operating costs of an asset. However, not all maintenance data should be used in a life-cycle cost analysis. The life-cycle analysis considers predictable, unpredictable, and reoccurring repairs and maintenance that are related to the actual operation of the asset. This includes preventive maintenance, inspections, breakdowns, warranty claims, and recalls. These are often called target costs.
Random and discretionary costs, such as crash repairs, user-requested modifications, weather-caused repairs, and vandalism are not included. These non-target costs may or may not occur depending on how well drivers are trained, where assets are stored, or what management decides. These costs are unrelated to the regular operation of the asset, often avoidable and controllable, and don’t occur at normal intervals related to age or use of the asset.
There are two types of traditionally defined non-target costs that you may or may not want to include in the analysis. One is physical damage caused by normal operation, which is different from damage caused by operator misuse — which should always be excluded since operators can be re-trained or taken out of the vehicle.
The other case is upfitting costs. Where some fleets capitalize these costs and include them in the overall purchase price of the asset, others will expense the costs. For fleets that do upfitting after delivery, the upfit accessories and attachments are purchased as parts and labor for the installation and are charged to a shop work order. Depending on the operation, these costs could be added to the capital value of the asset or paid for with expense funding. When the costs are included in the purchase price from the vendor or added later to the capital cost of the asset, the cost of prep and upfitting is amortized over the life of the asset and appears in the ownership side of the analysis. If prep and upfitting costs are expensed, the first-year maintenance costs can be overstated and skew the analysis. A best practice is to code this work as prep work and add it to the capital costs when calculating the ownership costs. Otherwise, consider leaving it out of operating costs and treat it as a one-time non-target cost.
The ability to distinguish between types of maintenance costs comes from how repair tasks or jobs are coded. Repair codes are often used to identify the type of repair. Having well-defined job reasons and teaching technicians and shop supervisors how to properly code jobs is critical to collecting accurate maintenance data. At the simplest level, technicians can flag jobs to be included or excluded from the life-cycle analysis.
Labor, vendor, and part costs over the years change. One of the data transformation tasks is to restate all historic costs in current-year values. This means applying an inflation factor and using a present value calculation to inflate the historic costs into the value in the current period.
An alternative to inflating labor costs is to use the total labor hours that occurred each month in service and applying the current labor rate to those hours. This will restate all historic labor costs to the present cost without having to look up inflation factors or run a complex function. This is also a good technique to use if you know that your labor costs historically have not been based on a fully burdened rate, or different methods have been used to calculate rates over time. In these instances, labor costs may not be accurately recorded and could understate actual costs.
Audit the data regularly to identify outliers that occur from entering data incorrectly, not logging on and off jobs correctly, or just fat fingers. When these show in the analysis, it can cause the monthly average to be overstated. Routinely running exception reports looking for higher-than-expected costs, or missing data, is a good data stewardship practice and will improve the quality of your analysis.
Other Operating Costs
Downtime is a method for applying reliability and maintainability to life-cycle analysis. The assumption is that assets become less reliable as they age, resulting in more frequent and longer repairs, and parts availability may become an issue as the asset ages and the OEM no longer supports it.
Fleet maintenance systems often will record downtime based on an asset’s operating schedule, or it can be calculated based on the open and completed date of the work order. This does require setting up operating shifts and assigning the proper shift to an asset or having a general 24/7 shift across all assets, which counts the full time the vehicle is out of service as downtime. It also means having good controls in place to ensure that work orders are opened on the date and time when the asset was taken out of service and completed when it is returned to service. This includes work done by vendors — it is not uncommon to see vendor work orders with the open and completed date minutes apart when the invoice is entered, where the vehicle was at the vendor for several days.
Downtime costs represent the cost of providing an alternative asset (a spare or rental) when a vehicle is out of service for repair. An hourly rate is determined from a rental rate, or from internal costs for maintaining a spare. Or a standard rate can be assumed for the fleet to factor in costs. The rate is applied the average hours down in each month in service to determine the monthly cost.
Fuel is not going to have much influence on the replacement cycle output of the life-cycle model, but it can be important when comparing the life-cycle costs of similar assets with different fuel types. There is an assumption that fuel efficiency declines over time. Our experience is that decline is not significant enough to change replacement cycles. However, there is a difference in the cost and efficiency of different fuel types across similar assets (e.g., compressed natural gas versus diesel or diesel versus gas)
Fuel prices can be very volatile over time depending on market factors. If actual historic fuel cost is used, those costs need to be restated in current-year dollars before running the analysis. To take out the price volatility and avoid recalculating costs, use the average monthly quantity and standard price to calculate the costs in current dollars. It is also important to include all fuel supplied from both internal pumps and external vendors to get the full history of consumption. Meter readings should also be linked to fuel transactions.
Focus on a Few Categories
Setting up data and running an analysis on categories that only have a few assets is time-consuming and will not return reliable replacement cycle results. However, plotting the operating costs can be useful to identify if there is a point in the life of these assets where major repairs are needed that could be a point for replacement. Unless the fleet has an automated life-cycle tool, fleet management should focus the regular analysis on the few categories that make up the majority of the fleet, and the categories that are expensive to own and maintain. This is where fleet managers have the best opportunity for identifying an optimal replacement cycle and build back-up to the replacement budget request.
About the Author: Marc Knight, CAFM, is the senior industry advisor for AssetWorks, which provides fleet management software.