TCO is not a static number

During the course of a vehicle’s service life, TCO is subject to change due to a vehicle’s age and numerous external factors. For instance, external market factors cause fuel prices to fluctuate, commodity costs to rise exerting upward pressure on tire and part prices, and depreciation rates tend to parallel wholesale market conditions and consumer buying preferences. These external factors can influence the TCO competiveness of one vehicle segment vis-à-vis another. For example, lower fuel prices are changing TCO considerations by lowering truck operating costs and increasing resale values, while negatively impacting residuals for compact sedans and hybrids.

TCO also influences vehicle replacement policies. Nearly all fleet-related expenses, both fixed and operating, are influenced by when a vehicle is taken out of service. The long-term trend in vehicle cycling by commercial fleets is a gradual increase in the service lives of vehicles. Over the years, automotive OEMs have dramatically improved vehicle quality and lengthened powertrain warranties, allowing companies to more confidently extend the service life of less risky fleet assets, such as fleets operating light-duty trucks.

Industry data reveal that light-duty truck and cargo van fleets have steadily pushed out their months-in-service parameters; with sedan fleets likewise extending asset use but at a slower average rate. Many fleets, especially mid-size fleets, are now moving to an 80,000-mile replacement parameter as fleet operations are pressured by senior management to rein in capital expenditures. Often, replacement cycling is only seen by management through a financial prism and viewed as a dollars-and-cents lifecycle cost decision. It is very common for companies to cut fleet costs by extending vehicle replacement cycles so the deferred cash flow can be diverted to other corporate expenditures.

Traditionally, fleet replacement policy is expressed as a combination of time and mileage, such as months-in-service and mileage bands. The policy is based on the converse trending of fixed and variable expenses during the life of the vehicle. Fixed costs tend to decelerate as a vehicle ages, while variable/operating costs tend to increase. When these two costs are charted over the life of a vehicle, the conventional wisdom is to replace a vehicle when the descending fixed cost line intersects the rising variable cost line.

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 now refer 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.

A flexible replacement guideline can take diametrically opposite directions. One direction could be to extend a replacement cycle as a short-term solution to compensate for a reduction in a capital expenditure budget. For example, service fleets, which have higher cap costs due to additional upfit equipment, will keep vehicles in service longer than light-duty fleets to control capital expenditures.

The other direction in a flexible replacement guideline is to shorten service life to shortcycle vehicles to take advantage of an exceptionally strong resale market, as was the case several years ago. Some fleets have decided they don’t want to be locked into a specified vehicle replacement cycle and prefer to remain nimble with a market-driven flexible replacement strategy. This mindset believes it is financially prudent to have greater flexibility in replacement cycling since extenuating market circumstances may make it more advantageous to either keep vehicles in service longer or shorter, depending on market conditions.

Vehicle connectivity and more sophisticated predictive maintenance data analytics will prompt a shift away from TCO to a more precise methodology focused on the “lowest cost of ownership” (LCO). In the final analysis, the most accurate lifecycle analysis in making the initial acquisition decision up through the best replacement strategy is one based on real-time data coupled with historical data.

Predictive modeling based on vehicle data continuously streamed by telematics will increase fleet efficiencies. For example, a study by IBM found that predictive analytics could cut fault diagnostic times by 70% and repair times by 20%.

Predictive modeling will provide the ability to better manage downtime and maximize fleet utilization. This will transform the traditional preventive maintenance model that is time- and mileage-based to one that is data-driven. Long-term, this will usher a shift away from a traditional reactive maintenance program to predictive maintenance model. In the future, repairs will be only completed before a disabling failure, so the utilization of vehicle assets will be maximized.

Predictive maintenance technology doesn’t prevent failures; it provides early warning of future failures that allows managers to decide where and when to repair before a failure occurs. This will help to minimize unexpected downstream failures and reduce driver/vehicle downtime. Ultimately, this data will be used to forecast the optimal time to replace a unit.