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How your plan assigns capacity

This page refers to the advanced planning features that are only available as part of Jira Software Cloud Premium and Enterprise.

We have a separate section for documentation about the project timeline that’s included in all Jira Software projects. Go to the documentation for project-level roadmaps in Jira Software.

As you assign issues to teams and sprints, your plan tracks the total amount of assigned work per iteration to help you make a viable plan of attack for your teams. It takes the estimation values (in story points or in hours/days) and subtracts it from the total capacity of your iteration. Learn how to change the capacity of your iterations

In order for capacity management to work, issues need to have an estimate. Learn more about estimating issues in your plan

The simplest way to manage capacity is to assign an issue to sprint using the Sprint field. Assigned issues consume the estimation value from the capacity, assuming they’re only scheduled to last one sprint.

your plan can also infer when an issue should consume capacity even if the Sprint field is empty. If an issue assigned to a team is scheduled during one of their sprints, the issue still consumes capacity--even if the Sprint field is empty. These issues are indicated with the label x issues not assigned to sprint in the sprint detail window which you can access by selecting the sprint from the timeline.

Change the capacity of an iteration from your timeline in Advanced Roadmaps for Jira Software Cloud.

How your plan distributes capacity

your plan assumes that each team member can work on one story-level issue at a time. With that in mind, it distributes an issue’s estimate based on capacity of one team member, which it calculates this using the following equation, assuming the capacity of each team member is equal:

  • [team’s capacity per iteration]/[number of people in a team] = [capacity of one team member]

It consumes one team member’s capacity for an iteration, then moves onto the next team member, repeating the process until the work is assigned. If there is still more work to do, a new iteration is started and the process continues until all the issues have been allocated.

For example, if a team of five has a capacity of 20 story points, your plan calculates an individual team member’s capacity to be four points per iteration (20/5=4). With that determined, your plan then distributes a story-level issue with a value of 10 story points over three iterations as follows:

  • iteration 1: four points

  • iteration 2: four points

  • iteration 3: two points

However, the capacity distribution algorithm in your plan honors any manually configured end dates that you set for an issue more than capacity. If the scheduled duration of the issue isn’t enough for the individual team member’s capacity to handle, your plan distributes the capacity as outlined above until the iteration where the issue is due. In the last iteration, your plan allocates all remaining estimate values in order to make your deadline.

If the example above was estimated at 16 story points (instead of 10) and had a due date at the end of the third iteration, your plan distributes the capacity as follows:

  • iteration 1: four points

  • iteration 2: four points

  • iteration 3: eight points

To change this behavior, revise the end dates of your issues as to not exceed the capacity of your team members, or adjust your issue’s estimate. You might also break down your work into smaller tasks and assign it to multiple people.

How the Auto-scheduler distributes capacity

While these are the base concepts of capacity distribution, the Auto-scheduler has more a complicated logic when planning across multiple iterations. Learn more about how the Auto-Scheduler works.


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