0%

Choose between FTE and Headcount

FTE or Headcount

Albert offers two couting methods: FTE (Full-Time Equivalents) and Headcount.

By integrating FTE data into your database, you can not only use this method in your projects, but also switch between the two methods within a single project, according to your needs.

But how do you choose between these two approaches? How can the use of FTEs, which incorporate part-time details, enrich your analysis? Let's explore this together.

Accuracy of demographic forecasts

Using FTEs enables greater granularity in forecasts, as results are rounded to two decimal places, which can offer a more accurate projection of the population.

With Headcount, results are rounded to the nearest integer, which may be simpler but offers less detail, especially when aggregating data across multiple entity levels.

To find out more about how Albert calculates demographic forecasts using both methods, click here!

Workforce needs with Business drivers

Business driver rules can be positive or negative percentage variations on a more or less precise perimeter of the population.

On the chosen perimeter, the % variation impacts the entire workforce of the selected perimeter, whether a family of entities or just a segment.

Click here to find out more about driver rules.

In the case of business drivers with percentage rules, Albert's algorithm calculates impacts at the finest possible level, before aggregating them (rounded) at higher levels.

As with demographic forecasts:

  • In FTE, results are rounded to two decimal places, and therefore closer to the results you'd expect at macro level.

  • In headcount, results are rounded to the nearest whole number.

So : FTE or Headcount?

Here's a summary of the reasons for using either method:

ETP

Effectifs

  • You need a more detailed and precise analysis of the workforce and its projections

  • You're using percentages and the project's cross-segments are very sparsely populated *

  • You want to accurately represent part-time employees

  • You would like to include an absenteeism assumption with an impact on FTEs **

  • You want to interpret the analyses easily (without decimals)

* Example:

  • The “Buyer” job segment / “Paris” geo segment contains 2 employees whose total FTE is 1.5.

  • This crossover is impacted by a +5% percentage driver.

  • The FTE requirement will be 1.55.

  • The headcount requirement will be 2 (no change)

** Example :

There is a recurring absenteeism rate among part of your population, and you wish to integrate this element. For example:

  • The absenteeism rate is 5% and affects the buyer population.

  • All buyers are full-time, so their FTE should be 1

  • Include the absenteeism rate by increasing their FTE to 0.95.

0%