FTEs REs and Headcount

Full Time Equivalents, Resource Equivalents, and Headcount are all different ways of looking at utilization in your organization. They fulfill unique roles and answer different business questions. This page will help you learn what each one does and when you are likely to choose one over the other.

Full Time Equivalents (FTEs)

Full Time Equivalents, or FTEs in Projector parlance, are a construct of the utilization report. Remember that at its core, the utilization report is driven by roles. Every role has a cost center, department, location, and resource. The utilization report takes hours on roles and tries to figure out how many full time people you would need to deliver those hours. A simple example is 120 hours. If you work 40 hour weeks, it would take 3 FTE to deliver those hours. 

This type of measurement is useful for capacity planning. How many Software Engineers do I need to deliver all my scheduled work next month? Do I need to hire or bench anyone?

So what is the exact definition of an FTE? An FTE is the number of people required to deliver X hours for location Y. That location's holiday schedule is taken into account in these calculations. For example, it takes 1 FTE to deliver 32 hours in a week with a holiday. Also 1 FTE to deliver 40 hours in a week with no holidays.

When looking at FTEs, you must also consider your reporting time interval. If your report is looking at months - then 1 FTE is what it takes to deliver all the hours for that month. If your report looks at weeks or days, 1 FTE delivers 1 week or 1 day.

For named roles, data fields like Working FTEs or Normal Working FTEs are the ratio of a resource's expected working hours and what that resource actually works. For example, if Kim works four day rather than 5 day weeks, her Normal Working FTE is 0.8. For unnamed roles, these fields don't make much sense. Rather, unnamed roles only show the hours booked against that role vs. the role's preferred location. Finally, unnamed roles show negative FTEs and represent demand that needs to be fulfilled. For example, next month you book five unnamed software engineers. You have negative 5 FTEs required. You also have 3 software engineers on staff who are all completely available. Your net FTE is negative 2, indicating that you need to bring some more capacity onboard to fulfill next month's obligations, push work out, or get some overtime scheduled.

Both holiday time and scheduled time off are treated the same as projected hours when computing FTEs.

Speaking of forecasting availability, there are two data fields that you will likely want to leverage when looking at demand and supply of labor. They are called Available FTEs and Net Available FTEs. Differentiating between these two can be confusing. So I'll give you a concrete example. Let's say Sally is overbooked for 12 hours on Monday and underbooked for 4 hour on Tuesday. Is she available or not?

  • Net Available - Sally has zero availability. Between Monday and Tuesday, she is scheduled to work her normal 16 hours.
  • Available - Sally does have availability. She can still work the second half of Tuesday.

Resource Equivalents (REs)

Resource Equivalents, or REs, are similar to FTEs, but fulfill a different purpose. They are used in the Ginsu and Baseline Variance report as opposed to the Utilization report. Whereas FTEs look at how many full time resources are needed to deliver hours on roles, REs tell you what portion of a resource is devoted to delivering a block of hours. That might sound pretty much the same, but there is a very important distinction.

  • RE - hours compared to the resource's schedule
  • FTE - hours compared to a role's location

Often they will be similar since a resource typically works the full hours for a location, but that doesn't have to be true. Imagine you have two half time interns. Together, they represent 1 FTE, but when looking at them individually, they are 1 RE.

The other important distinction is that REs let you drill down further than an FTE. All the way to the sub-project level. For example, how much of Tom's time is dedicated to delivering task X? .75 RE means three quarters of his time is spent there. When multiple resources are grouped together, their REs are aggregated. So delivering this project next month might take 10 REs. This is the reason we don't display REs as a percentage. Aggregating percentages makes less sense. You wouldn't say you need 686% resources to deliver this project. You would say you need 6.86 resources.

We most often point users to REs when they are looking for "utilization at the project level." As you'll recall, utilization at the project level isn't a straightforward concept, so REs are a handy way to look at it in a different way. Rather than seeing Tom's utilization split as 10% on project 1, 70% on project 2, and 20% on project 3 - you see that Tom has .1 RE, .7 RE, and .2 RE instead. 

One last thing, which is that REs can go over/under when looking at data in the past. For example, if Tom works overtime, then he will have more than 1 RE. 

Like FTEs, when looking at REs, you must also consider your reporting time interval. If your report is looking at months - then 1 RE is what it takes to deliver all the hours for that month. If your report looks at weeks or days, 1 RE delivers 1 week or 1 day.


Headcount is metric that looks useful and a lot of people gravitate towards it. However, it is a field we often steer people away from in favor of FTEs and REs. The reporting time interval affects how we do headcount and the methodology we use ends up not being very useful. For example, say your report is looking at headcount by the month. Projector returns a whole value (1) for each resource that is in that month. Imagine Jim works 1 hour in June and Sally works 100 hours in June. Each give you a headcount of 1, for a total of two. FTEs and REs are much more useful as they give you weighted values relative to the time period.

Double (or triple) Headcount

Pivot tables aggregate data by looking at each unique row in the pivot cache and summing them. If you are drilling into headcount through multiple row fields, this can cause your headcount to be too high. For example, your report shows headcount, by title, by month. Sarah's title changes from Manager to Sr. Manager in the middle of the month. This generates two rows in the pivot cache. One for each title. Her headcount will be 2 for that month and likely not what you want.