# How your plan assigns capacity

To use capacity planning in your plan, you need to enable it and your plan needs to meet certain criteria.

To count against the capacity of an iteration, issues need to have values in all three of these fields:

• Team

• Sprint

• Estimation (hours/days or story points)

As you assign estimated issues to a team’s sprints, your plan subtracts the estimation values from the total capacity of the iteration. Find out how to change the capacity of your iterations.

## Scheduled issues without a sprint value

Issues assigned to a team and scheduled during their sprint consume capacity regardless of whether you add a value to the Sprint field. The label x issues not assigned to sprint in the sprint capacity detail window refers to these issues. Find out more about the sprint capacity detail window.

## Scheduled issues without an estimation

Issues assigned to a team and sprint without an estimation show next to the label x unestimated issues in the sprint capacity detail window. Find out more about the sprint capacity detail window.

## 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. Read more about the Auto-Scheduler.

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