How to convince your boss that your team is overworked

This article is written by Josh Solinger, operations data analyst, Pierce County, Wisconsin

All public servants who have taken part in budget negotiations will have experienced this in one form or another: on one side of the table is an operations manager pleading for more staff. Perhaps their staff report feeling overwhelmed, they expect service demands to increase, or they want to bring in new staff to train in anticipation of staff departures. On the other side of the table is the executive leadership, tasked with managing more budget requests than there is funding for.

• Want to write for us? Take a look at Apolitical’s guide for contributors

While the conversations about staffing needs and fiscal constraints rarely lack passion, they often lack actionable data. Without compelling data, it is hard for staff to push for increased hiring, which often means it doesn’t make it into the final budget. This may leave staff feeling frustrated, and they’re confined to their frustrations for the year, only to repeat the process during the next budget cycle.

But what if this process was improved with good data? Although good data doesn’t guarantee outcomes, it can move conversations from anecdotal and floundering to data-driven and impactful.

How long does it take to mow a lawn?

In this piece, I will introduce you to the concept of using data to model workload indicators and estimate future staffing levels based on the workloads.

A workload indicator shows the volume of work being completed by each full-time employee, and can be projected into the future. This specific model, which is a modification of a model developed at the City of Eau Claire, Wisconsin, provides a way to project future workloads and estimate future staffing levels needed to accommodate the volume of work. The end goal is to get everyone at the budget discussion table on the same, concise page regarding staffing.

It is up to each organisation to determine what a suitable workload looks like for their staff.

To start developing a workload indicator that shows the volume of work completed by each employee, you first need to define the service that you will measure. For instance, in a local government parks department, definitions might include the volume of parks acreage mowed, hours of playground repair, or lane miles of streets plowed. Next, you need to know the amount of time spent by employees on the services you just defined.

To calculate the total amount of time spent on a service by employees, I suggest totaling the number of actual full-time equivalents (FTEs) utilised. Thus, if one full-time (2,080 hours) employee equals one FTE (1.00 FTE), and they recorded 1,248 hours on the service, you would allocate 0.60 FTE. Table 1 shows how this is calculated for multiple employees.

Isolating the data to one specific service you want to measure can be challenging. Hopefully your organisation has unique codes for each service that employees utilise when completing timesheets. That data, when compiled by your payroll or accounting staff, can be retrieved and used to complete the calculation shown in Table 1.

If your organisation does not explicitly track time for each activity, one solution is to ask staff to document their time for a long enough period that it becomes a good representative sample of a whole year. Alternatively, you could ask staff to recall this information from memory, but beware that this method is the likeliest to produce a large margin of error. In any case, there will be some margin of error, but as this process becomes a normal part of your organisation, the error will become smaller over time.

Now, let’s say that the service you’re interested in examining is the quantity of parks acres mowed (one acre is equivalent to 0.4047 hectares or 4,047 square metres). You know that you have 2.62 FTEs mowing parks. After gathering information on park acreage, you find that there’s a total of 250 acres of parkland that requires mowing. A simple calculation of dividing the number of mowed parks acreage by FTEs reveals that you have roughly 95 acres per FTE. It’s now time to benchmark this data.

Perhaps through a combination of surveying other organizations, surveying your own employees, or another method of standardising parks maintenance, you determine that it’s suitable to have roughly 70 acres per FTE. It’s unlikely that your community wants to reduce the number of parks acreage available to residents.

Another solution to get from 95 acres per FTE to 70 acres per FTE is to increase the number of FTEs mowing parks. Re-allocating the time spent on other activities to mowing parks won’t work if your staff is simply maxed out. In this case, hiring additional staff becomes an option to consider further.

By adding a single FTE to mowing activities, or increasing from 2.62 FTEs to 3.62 FTEs, you find that the number of acres per FTE changes to 69 acres per FTE (Table 2). This fits with what you determined is a suitable ratio of acres to FTEs! Easy enough, but what about marrying this analysis to the organisation’s future capital expenses? In this example, the capital expenses you’re concerned with would be future acquisitions of additional parkland that would add to the acres requiring mowing.

Although adding a single FTE for mowing activities brings the ratio of acreage per FTE to a suitable level for now, you already know that your community will be adding a significant amount of park acreage in the coming years (Table 3). The organisation’s five-year capital improvement plan (CIP) calls for 100 acres of parkland to be acquired over the next five years. While one additional FTE works for now, it won’t work for long.

By establishing the workload ratio for one year, you’ve created a foundation to map out the workload ratio across multiple years. The calculations are simple, and once you marry this data to the planned capital outlay, you’ve created a forward projection of service demand. You can then adjust the number of FTEs needed to keep the ratio of parks acreage per FTE to around 70 (Table 4).

Based on the forward projection of service demand, the organisation should add 0.50 FTE to mowing in 2022, and an additional 0.75 FTE to mowing in 2024. This enables the organisation to maintain a workload ratio of around 70 acres per FTE, which the organisation determined is manageable. It’s possible that other aspects of parks maintenance, such as tree pruning or ballfield preparation, will increase as parkland is acquired. Thus, it’s reasonable to recommend hiring a full FTE in 2022 and 2024. The organization can allocate excess time not needed for mowing to other parks maintenance activities.

It’s not just a model for mowing lawns

This example utilises the relatively straightforward activity — from a measurement perspective — of mowing parks. However, my own recent success with modeling future projections for workload and staffing levels also demonstrates the model’s flexibility. In this case, I was tasked with providing an objective recommendation for mental health staffing after elected officials and mental health staff arrived at an impasse over whether to add staffing.

All in all, workload indicators and forward projections of staffing levels are valuable tools to maintain throughout the cycle of budget planning, implementation, and year-end review.

In this case, the workload measurement involved the number of adult and child clients assigned to each mental health professional. In addition, because each client presented with a unique set of circumstances, workloads for each individual client varied. Also, as new mental health staff are brought onboard, they are unable to accommodate a typical workload for the first year. This is because of the time required to learn all of the State and Federal regulations involved in patient privacy and mental health services.

As more information was acquired, the model was fine-tuned beyond the basic approach presented in this paper. Projections of future client demand were added based on both past growth in the demand for services, as well as feedback from staff. Future staffing levels needed to maintain an adequate ratio of clients to staff were then projected. The determination of what ratio is adequate was made by including feedback from both staff and other organisations.

The end result was a model that showed that, although more staffing is not necessary at this time, it will be within the next two years and the organisation should prepare accordingly. Both staff and the elected officials accepted the data and the impasse came to an end. I am confident that this model can produce similar success for other organisations.

Mapping indicators also takes time

Having workload data does not guarantee a desirable outcome during the budget process. However, when accompanied by good data, requests for additional resources become more compelling. I believe this type of approach has been lacking in local government because, while operations staff are very passionate about their work, their arguments are often rooted in passionate anecdotes about their service, rather than the data that decision-makers need to see.

Although the operations manager may not get their wish, by tracking this type of data over time, leadership can be presented with evidence of growing strain on operations staff during the next budget cycle. This will provide an increasingly more compelling argument, and lets staff know they are being represented during budget discussions. For these reasons, I hope this type of model becomes more widely adopted in local government.

It is up to each organisation to determine what a suitable workload looks like for their staff. The numbers used in this example are for illustrative purposes only. And the amount of work that goes into developing a proper target workload should not be underestimated.

Surveying peer organisations is a good start, but care should be taken to also recognise the unique qualities of your community and then take those into account. In addition, understanding the priorities of your elected officials and community members through periodic surveys is also important. Why add staff to a service that the community doesn’t prioritise?

All in all, workload indicators and forward projections of staffing levels are valuable tools to maintain throughout the cycle of budget planning, implementation, and year-end review. If you have any questions about this process, please don’t hesitate to reach out to me. — Josh Solinger

We want to hear what you think about this article. Pitch an article to us or send your comments to [hello@apolitical.co](mailto:hello@apolitical.co)

(Picture credit: Unsplash)