I’m currently reading the advanced ScrumBan book Scrumban [R]Evolution – Getting the most out of Agile, Scrum and Lean, Kanban by Ajay Reddy and found a nice extension of Little’s Law that I would like to share with this post.

#### A short recap on Little’s Law

*Formula 1*${\mathrm{average\; output\; (per\; time\; period)}=\frac{\mathrm{average\; inventory\; of\; work\; (per\; time\; period)}}{\mathrm{average\; lead\; time\; of\; work\; (per\; time\; period)}}}$

The average inventory of work is the average number of user stories between the starting and end points for a given period of time (WIP).

The average lead time is the average amount of time it takes for a work item to move from the first stage of a production process to the end of that process.

#### And the extension for release planning with Little’s Law

*Formula 2*$${\mathrm{Average\; WIP}\phantom{\rule{0.2em}{0ex}}\text{(The amount of developers,resources,\u2026 needed)}\phantom{\rule{0.2em}{0ex}}=\phantom{\rule{0.2em}{0ex}}\phantom{\rule{0ex}{0ex}}\mathrm{Average\; Lead\; Time}\phantom{\rule{0.2em}{0ex}}\left(\frac{\mathrm{number\; of\; user\; stories}}{\mathrm{Total\; project\; time}}\right)}$$

*Formula 3*$${\mathrm{(Project/Release)\; lead\; time}\phantom{\rule{0.2em}{0ex}}=\phantom{\rule{0.2em}{0ex}}\left(\frac{\mathrm{number\; of\; user\; stories}}{\mathrm{Average\; Throughput}}\right)}$$

test

Let’s consider the following example… Given:

- each team member averaging 2 items of work in progress at any point in time
- a teams average completion rate of 28 user stories per 2 weeks iteration (14 stories per week)
- an average lead time of 0.9 weeks per user story
- the release backlog with 675 user stories

This means you’ll need 24/2 (average WIP of every team member) = 12 team members to accomplish the project within 26 weeks.

And what happens if you have to finish it in 18 weeks?

This means you’ll need 34/2 = 17 team members to accomplish the project within 18 weeks.

Now let’s fix the number of people to 12 (2 teams with 6 team members each) and we need to forecast the project duration.

What a nice extension for release planning.

In addition we need to consider that work delivery rates in projects are not uniform but tend to follow a fairly predictable S-Curve (with delays in the beginning and at the end of a project).

Little’s Law can therefore be applied with high confidence to only the middle portion of most projects (approximately 60% of total project duration).

For the remaining 40% of the project we need to work with a project buffer that can be calculated using the formula I’ll describe in my next post (just subscribe to receive your weekly updates right in your inbox ;-).

#### Further readings

Add on 2015-10-25: