Is there a "best practice" formulaic method in which a public sector entity can reliably justify their staffing requirements? - Photo: Government Fleet

Is there a "best practice" formulaic method in which a public sector entity can reliably justify their staffing requirements? 

Photo: Government Fleet

For decades, the automotive and truck repair industry has been plagued by a shortage of skilled technicians. The shortage has been particularly hard on government fleets for reasons both well known to public sector fleet managers and over much of which they have little or no control; a truly frustrating dilemma but characteristic of the current state of play in most governments.

Understanding Technician Shortages Within the Fleet Industry

Some experts have opined that within this long period of shortage there have been brief periods when the shortage has not been the result of a lack of skilled labor in the market but instead the result of poor retention efforts or some lack of creative recruiting intentionality, or an outgrowth of economic instability, or the loss of educational, technical tracks such as shop class in high schools in favor of directing students to four-year colleges, or the most popular excuse, COVID.

Fortunately, technical education in both public schools and technical colleges is enjoying a renaissance, and these factors have all had some influence, but the math does not lie. More skilled technicians are leaving our industry than are entering it.

Public sector fleets suffer from actual staffing losses, especially in skilled positions at every level. This situation will worsen as the labor market suffers a dearth of blue-collar interest among the even fewer folks who enter the labor force due to the "Enrollment Cliff" starting in 2025.

Further complicating this labor market reality is the lack of an easily calculated, easily explained, routinely accepted, and acknowledged industry standard for determining and justifying the appropriate staffing levels in public sector shops.

Consequently, there is no "best practice" formulaic method in which a public sector entity can reliably justify their staffing requirements as it pertains to the number skilled technicians required to achieve and maintain service levels.

If you have reached this point in this article, you may be thinking that such formulas do exist. There are four formulas, three very similar, utilized by fleets for decades to justify staff levels, albeit for many, unsuccessfully.

A Quick Look at Each of the Four Formulas

Technician to Vehicle Ratio:

This is a simple calculation where the total number of vehicles in a fleet is divided by the number of current technicians resulting in a ratio, for instance, one technician for every 35 units. The fallacies in this calculation should be clear.

First, if using this ratio to compare your staffing with another fleet, it presumes the number of technicians currently on staff at the neighboring agency is correct (offering no help in justifying your different number), but worse, it presumes both fleets are identical which is never the case. If one fleet is heavy in law enforcement vehicles or has a refuse component and the other does not, the two are far from identical; their technician ratios will always vary, and for good reason.

Unfortunately, governments are great copycats. Governments like to compare themselves with their neighbors, making the ratio method a common denominator favored by many due to its simplicity. The devil is in the details, however, which makes this method ill-advised and, unfortunately, renders the comparative variances hard to explain to senior leaders.

While understood well by fleet, those variances are harder to explain to decision-makers who may not know fleet as well.

Vehicle Equivalency (VEU):

This is a time honored method recommended prominently by NAFA and the APWA for decades but unfortunately, it too has passed its prime as a useful measure.

This method assigns a numerical value (e.g., Vehicle Equivalency Unit or VEU) to various vehicle and equipment types based on the degree of repair difficulty or hours needed to maintain readiness. For instance, a sedan may be assigned a VEU of "1" and a refuse truck a VEU of "10," acknowledging the higher degree of maintenance difficulty for a refuse collection truck.

Following this numeric assignment by category, the fleet then multiplies the number of units in each assigned category by their VEU assignments to arrive at a total VEU score per category. Those totals are then added together to arrive at the total number of VEUs for the entire fleet, which is then divided to arrive at a usually fictitious number representing how many technicians are needed to properly maintain that quantity of VEUs.

This method, too, has many flaws. The most glaring of which is that in today's fleet environment, the VEU calculation presumes and accepts that the fleet operation for which the calculations are based is operating at some level of acceptable efficiency.

The calculation accepts business as usual by basing their VEU calculations on their own performance. And what manager does that? Another way of stating this anomaly is when a mediocre fleet bases any measurement standard on itself, the results will be equally mediocre. 

The VEU method does not account for high or low technician productivity levels, vehicles under warranty, current staffing that may feature a majority of technicians with high or low amounts of accumulated leave time, vehicles featuring high levels of technology or repair processes required for ADAS or other advanced tech functionality, or the varied skill levels of technicians.

Maintenance Repair Units (MRU):

This method is essentially a modified VEU method that allows liberties with the same flawed methodology noted above. It too is dated by failing to account for new technology and processes as it too was introduced in the last decade.

Processes have changed. Even in today's high-tech repair environment, a root cause analysis may extend repair time as the diagnostic tree is followed in detail. No allowances have been made for this now routine process for many repairs.

Technology and Maintenance Council Recommended Practice RP512-a:

This is a slightly modified version of the VEU and MRU methods released in 2017 by the TMC. Here are the steps:

  1. Determine the labor needed for your current fleet. Vehicle labor hours are figured by dividing the total labor hours needed (TMC includes vendor hours in this calc., which seems an odd variable to include).
  2. Separately calculate the total vehicle hours needed for each type, make, vehicle class and model year of each piece of equipment and add the results.
  3. Calculate the number of direct labor hours.
  4. Take the total number of labor needed (step 2) divided by the number of direct labor hours per day (step 3).

While this method may seem easy, this process can take hundreds of hours for a large and diverse fleet typical in a government fleet. Further complicating this process is that as vehicles are replaced or updated with new equipment, and/or the number of skilled techs changes, or the warranty factors are extended this calculus will change.

It should also be noted that the above methods were created when vehicle maintenance was 100% hands-on dependent. In today's world, vehicles are becoming increasingly software-intensive where some maintenance functions are, and surely more will be in the future, software, over-the-air generated or facilitated by the use of sophisticated diagnostic tools.

Each of these methods presupposes that the hours captured on work orders (and the vendor hours in the TMC example) are accurate and applied to each task correctly, regardless of the skill level of the technician who performed each repair, which is seldom the case.

Each method fails to include warranty allowances, driver error repairs, vehicle specification variations in otherwise similar vehicles, or other time-consuming variables that, while not being directly applied as wrench time, occur in most shops nevertheless.

The primary argument against using these formulas is that they all use the current fleet's history, repair hours, and performance by basing their calculations on the presumption that their current performance is at an acceptable level.

This presumption thereby discounts the possibility that performance can be improved by allowing for variables that may improve performance, productivity, efficiency, training or skill levels or a myriad of other modifications that should, in themselves, be objectives in any fleet's improvement strategy. Accepting the status quo should never be the objective and self-benchmarking against your operation alone is never recommended.

This lack of a justifiable staffing model results in a further dilemma for fleet managers beyond that of actually sourcing and recruiting technicians. They lack clear, understandable and actionable results on which our elected officials and even our bosses can rely in order to support our calculations and their decisions on staffing requirements.

Finally and perhaps most importantly, once this exercise in data collection has been concluded, it is very hard to find any other fleet that has engaged in this very exercise and can thus be held up in comparison to yours. Consequently, even senior management and/or elected officials cannot validate your process and accept it as justification to amend staffing. Where does that leave you?

Rethinking the Formula to Determine Staffing

Is there a better, more relevant formula to determine staffing levels?

Every fleet that performs its own in-house maintenance activity should recognize that core competency must be, first and foremost, preventive maintenance. Every maintenance activity in which their shops engage should either originate as a result of a PM inspection or be performed with the objective of assuring that the repair is performed well enough not to be repeated, at least not in the near term future. 

Here is a graphical example of how PM emphasis should look. - Photo: Bob Stanton

Here is a graphical example of how PM emphasis should look.

Photo: Bob Stanton

Most fleet professionals agree that any proactive maintenance program's foundation is quality preventive maintenance. Should that also be the foundation of your staffing formula? The time to perform a PM inspection, regardless of the level (A, B, or C, etc.), varies little, making them fairly standard among similar vehicle types.

If it can be agreed that PM's are the first priority for a maintenance operation, then staffing levels must be at least adequate to perform all scheduled PMs on time for that fleet.

Using this step-by-step example, your staffing formula could progress in this way:

Step #1: By vehicle type, mileage and PM types common in any budget year for each vehicle, determine how many hours should be allocated to your PM program to meet your PM schedule on time.

Step #2: Using the current technician population, calculate how much leave time, in total/year, will be lost to vacations, holidays, personal leave, other indirect time such as lunches, breaks, training, meetings, and other non-repair activities. This is a critically important calculation. While many fleets presume the "time-honored" 75% productivity rate of 1560 direct hours (75% of 2080 annual payroll hours) should be your targeted productive rate for each technician if you have a mature (long-tenured) staff, or generous leave allowances, the 1560 target may not be achievable at your shop. On our best day, for one fleet this author managed, the best productivity rate possible was 68%. Most of the technicians were 20+ year employees. Use your actual number, not someone else's arbitrary target.

Step #3: Determine your base PM technician staffing level by dividing the number of PM hours needed by your actual available direct hours/technician. Total PM Hours required/ex. 1560 hrs (or your actual number) = number of techs for the PM program. This calculation provides the base number of technicians needed to execute your core competency, which is your PM program.

Step #4: You already have your vehicle categories determined (Step 1), using your maintenance history as an average, determine how many hours, over and above PM time, each category of vehicle requires per unit to maintain your readiness targets. For instance, a police SUV may require an average of 15 more hours/year for repairs beyond PM's, a refuse truck may require 50 - 75 more. Some categories (e.g. fire, refuse, landfill equip.) will be high, others (e.g. small trailers) much less.

Using your average repair hours/vehicle category, total those hours for your fleet and divide that total by the same direct hours figure used in Step #1. Total Repair Hours required/direct hrs/tech = total techs for Repairs.

Step #5: Step #2 plus Step #4 = Total Techs required.

The benefits of using this method are clear.

  • FMIS systems can update repair hours by vehicle category. From the use of this matrix, fleet managers can better manage changes in their staffing levels.
  • Identifies preventive maintenance as an imperative, rather than being relegated to merely a scheduling headache that gets less emphasis.
  • Allows for changes in vehicle inventory composition. As categories areupdated by new or additional units, the total staff/repair hours required will change. Conversely, if a replacement cycle is delayed or missed, the impact on staffing requirements due to more frequent repairs needed on the older units can be more readily identified.
  • By using repair hours by category, fleets have a more detailed and easily explained method of sharing staffing needs.
  • Brings clarity to budgeting for labor and prospective overtime by clearly predicting where repair hours should be dedicated either to a vehicle category or facility.
  • By focusing on hours by vehicle category, managers can concentrate on improvements directly related to categories of need in training, parts availability, modifying vehicle specs, and the dedication of efficiencies in an attempt to reduce repair hours in those categories of opportunity.

New technology demands updated measures and an acknowledgment that new tech means different and newer repair processes. That 100% hands-on maintenance is becoming less common. EVs are not the only vehicle category where less hands-on care is needed.

Is it time to rethink the formula?

About the author
Bob Stanton

Bob Stanton

Fleet Consultant

A charter member of the Government Fleet Hall of Fame, Bob Stanton spent 25 years in government fleet management. He is currently an independent fleet consultant based in Cumming, Ga.

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