These are tough times. Companies of all industries and sizes face economic uncertainty. Leaders are preparing for a possible recession by reducing expenses and running more efficiently. Hiring freezes, inability to backfill employees and postponed projects are common these days. All of these mean most organizations need to figure out how to deliver results with less.
At ActivTrak, we use our workforce analytics platform to make data-driven decisions every day. This “drink our own champagne” philosophy has fueled numerous innovations that help identify common workplace problems like burnout, constant digital distractions affecting focus, workload imbalance and more. Our last quarterly planning cycle was a perfect opportunity to apply this philosophy to answer the question: “How can we use ActivTrak to make key workforce capacity planning decisions?”
Understanding your workforce capacity
Workforce capacity planning is a process that ensures you have sufficient personnel to deliver against your current and future workload. It provides visibility into whether you have the right number of employees with the right skills to meet the needs of the business. Unfortunately, this process is stuck in the stone ages, as most companies try to perform capacity planning without actual employee activity data, relying instead on finance-driven headcount forecasts, employee opinions and anecdotes.
With this in mind, we analyzed our internal productivity data to inform short-term workforce investment and efficiency strategies – exploring questions like:
- Where can we afford to not rehire or backfill?
- How do we redistribute or absorb work among the existing workforce?
- Who can take on additional work and which teams need additional support to deliver results?
It’s important to note this analysis is about capacity and not performance. You can have a top performer with bandwidth to help others and you can have a poor performer who is over capacity.
Workforce capacity data analysis
The data-driven workforce capacity planning approach we followed consists of three steps:
- Establish the expected capacity
- Analyze actual vs. expected capacity
- Identify opportunities and risks
1. Establish the expected capacity
The expected capacity (or Expected Total Productive Hours) for every team member is calculated based on two inputs:
- Productive Hours Per Day Goal (e.g. 6.5 hrs/day). This is usually set by the manager based on role and seniority. Learn more about ActivTrak Productivity Lab’s recommendations on how to set data-driven goals for your teams here.
- Expected Days Worked. This is the reasonable number of days employees are expected to work after accounting for holidays, vacation and sick days. For the full year, that was 260 weekdays minus 10 holidays, 20 vacation days and 8 sick days = 222 expected days worked. The “expected days worked” number is typically ~ 85% of the workdays in the time period.
Expected Total Productive Hours = Productive Hours Per Day Goal x Expected Days Worked
2. Analyze actual vs. expected capacity
We calculated the Total Productive Hours for each team member by multiplying the number of Days worked (Active Days) x the average productive hours per day. Then, we compared their Total Productive Hours to their Expected Total Productive Hours to determine their FTE or Full-Time Equivalent Capacity %.
User Capacity = (Total Productive Hours / Expected Total Productive Hours) * 100
3. Identify opportunities and risks
Using the metrics described above, we identified the following scenarios:
- At Capacity: If team members’ User Capacity was between 80% to 120%, we considered them to be “at capacity” and recommended they remain focused on the most critical tasks to maximize results.
- Over Capacity: If team members worked more than 120% of their Capacity, managers should support them by taking work off their plates and distributing it to team members who are under capacity or at less than 80% of Capacity.
- Capacity Available: If team members’ Capacity was less than 80%, they were deemed good candidates to take on work from over-capacity team members or any employees who have departed the organization/team.
A data-driven workforce capacity planning process
Here is an anonymized example using the approach described above, leveraging the productivity data from Q2 and Q3 2022 for one of our ActivTrak teams.
The insights above allowed the manager to collaborate with their team members to make the following adjustments:
- Freeman absorbed part of the responsibilities from another team’s employee that left the company in Q3.
- Kenya took over the coordination of a couple of customer programs from Michael to help reduce her burnout risk due to being overworked.
- Farrah will now be responsible for providing weekly reports on customer adoption that we were considering hiring a part-time contractor to do.
- William will work together with Marcy on a couple of upcoming projects to ensure the load is balanced between both of them.
In summary, this team was able to avoid two incremental hires (one permanent and one part-time), which resulted in annual savings of $250K and also significantly reduced the burnout risk of two high-performing team members. We saw similar results with other teams, so we have now incorporated this quarterly workforce capacity planning analysis as part of our operating cadence.
Workforce capacity planning is now in ActivTrak
Although the data we used in this analysis is available in the Workload Balance and Location Insights Dashboards, the analysis was not optimized for workforce capacity planning. That’s why we now have a new dashboard that makes it very easy to perform this analysis on a regular basis across all your teams.