In reviewing the history of fleet management, a single question stands out among all others as the most frequently asked: “How long should I keep my vehicles in service?”
The answer to this question changed very little from the 1950s to the 1980s. The average replacement cycle then was 55,000 miles — a maximum of 36 months for passenger cars and 42 months and 75,000 miles for trucks. The reasons for this cycle timing were quite simple — vehicle quality was average, at best, and a 55,000-mile replacement made sense because of maintenance costs, driver downtime, and resale deducts for vehicles with higher odometer readings.
Fleet replacement policy is most commonly expressed in a combination of time and mileage, i.e., a number of months or mileage level. The logic behind this policy lies in the progression of fixed and variable expenses during the life of the vehicle. Fixed costs tend to decelerate the older the vehicle, while variable costs tend to increase. When you chart these two costs over the life of a vehicle, the time to replace it is when the descending fixed cost line intersects with the rising variable cost line.
However, in mid-1980s, the standard paradigm for fleet vehicle replacement parameters started to lengthen. The reason for the shift was multi-dimensional:
Increasing prevalence of passenger minivans.
Emergence of the SUV.
Vehicle quality revolution.
Ever-increasing new-vehicle acquisition prices.
Decreased stigma to buying higher mileage used vehicles.
By the 1990s, the recommended replacement cycle had extended, but the recommendations became more granular. For example, companies started differentiating between vehicle applications such as sales, field service, work trucks, etc., as well as by vehicle product class, such as passenger car, minivan, SUV, luxury vehicle, etc. Replacement analytics became more sophisticated, as databases were now available to analyze maintenance and resale histories so economic value could be modeled to help guide the replacement decision.
What emerged was an understanding that the primary economic drivers of the replacement decision were:
Acquisition cost sensitivity (the higher the acquisition cost, the longer it paid to keep the asset).
Resale market performance.
Warranty coverage period.
Maintenance cost history and forecast.
Increasing sensitivity to the cost of driver downtime.
Seasonal influences on the resale market and depreciation factors.
Some of these factors, such as depreciation expense, tended to push the decision further out, while others, such as maintenance cost and driver downtime, tended to favor shortening the decision. Thus, the modeling became very sensitive to changes in these factors, to the point reached today, where technology and the speed of information enable the industry to increase the granularity of replacement recommendation down to the individual nameplate and even individual vehicle.
With this increased granularity has come increased complexity. For example, changes in resale market values can be seen in near real-time. Nowadays, new models are being introduced throughout the year as opposed to the traditional fall introduction. Fuel prices (or mpg) are a strong contributing factor in the resale value of vehicles. Today, lower fuel prices are benefiting truck resale values, while negatively impacting the residuals for compact vehicles and hybrids. Another factor is changes in consumer buying behaviors for new and used vehicles. Changes in competitive fleet allowances and consumer rebates have influenced acquisition costs and buying behavior. External forces, such as business boom-and-recession cycles, interest rate fluctuations, and credit availability for retail buyers of new and used vehicles have all contributed to the complexity.
As fleet continues to be one of the top five indirect spend categories for most companies, the need for economic optimization has never been greater.
As a general rule of thumb, below are a few of the principles that apply to determining replacement cycling:
Asset Cost: The more expensive the asset (upfitting included), the more it makes sense to keep it in service for a longer period.
Cost of Driver Downtime: The higher the impact of driver downtime, the shorter the vehicle replacement cycle. Driver downtime would include the cost of the driver, lost revenue, and temporary transportation.
Remarketing Seasonality: While external factors, such as fuel prices and swings in the economy, have affected replacement cycles in past years, the fall continues to be the best time to replace most asset classes.
Company Image: Body damage, rust, peeling decals, and breakdowns on the road occur with older vehicles. The condition of a company’s vehicle may be the first impression a customer or prospect may get when they see the vehicle. If a company markets itself as a high-quality repair business and the service van shows up with body damage and rust, the customer may relate the presentation of the vehicle to the lower quality of repair.
Retail Demand for Used Vehicle: Very popular retail vehicles can be cycled more rapidly to take advantage of favorable supply/demand factors.
Increasing Granularity
Today, a growing number of fleets are shifting to more flexible vehicle replacement cycles. In fact, some fleets no longer call their replacement cycle a “policy” and are now referring to it as a “guideline.” The rationale is that they want to reserve the right to determine when to take a vehicle out of service based on prevailing market conditions, rather than predetermined mileage and/or months-in-service parameters. Enabling this shift is the growing sophistication of lifecycle optimization modeling, in particular, the development of analytics to calculate the various “what if” scenarios to identify the optimal vehicle replacement parameters.
My prediction is that data analytics will take vehicle replacement cycling to a granularity that we, as an industry, have never before experienced.
Let me know what you think.
