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
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
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 ;-).
Add on 2015-10-25: