Help! Too Many Incidents! – Capacity Assignment Policy In Agile Teams

As an Agile coach, scrum master, product owner, or team member you probably have been in the situation before in which more work is thrown at the team than the team has capacity to resolve.

In case of work that is already known this basically is a scheduling problem of determining the optimal order that the team will complete the work so as to maximise the business value and outcome. This typically applies to the case that a team is working to build or extend a new product.

The other interesting case is e.g. operational teams that work on items that arrive in an ad hoc way. Examples include production incidents. Work arrives ad hoc and the product owner needs to allocate a certain capacity of the team to certain types of incidents. E.g. should the team work on database related issues, or on front-end related issues?

If the team has more than enough capacity the answer is easy: solve them all! This blog will show how to determine what capacity of the team is best allocated to what type of incident.

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Little’s Law in 3D

The much used relation between average cycle time, average total work and input rate (or throughput) is known as Little’s Law. It is often used to argue that it is a good thing to work on less items at the same time (as a team or as an individual) and thus lowering the average cycle time. In this blog I will discuss the less known generalisation of Little’s Law giving an almost unlimited number of additional relation. The only limit is your imagination.

I will show relations for the average ‘Total Operational Cost in the system’ and for the average ‘Just-in-Timeness’.

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Why Little’s Law Works…Always

On the internet there is much information on Little’s Law. It is described an explained in many places [Vac]. Recently, the conditions under which it is true got  attention [Ram11]. As will be explained in this blog the conditions under which the law is true are very mild. It will be shown that for teams working on backlog items virtually there are no conditions.

Why does it work? Under what conditions does it hold?

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Applying Little’s Law in Agile Games

Have you ever used Little’s Law to explain that lower WiP (work in progress) limits lead to shorter cycle times? Ever tried to illustrate Little’s Law in an Agile game and found it doesn’t hold? Then read this blog to discover that it is exactly true in Agile games and how it really works.

Some time ago I gave a kanban workshop. Part of the workshop was a game of folding paper airplanes to illustrate flow. To illustrate Little’s Law we determined the throughput, cycle time and work in progress. To my surprise the law didn’t hold. Not even close. In this blog I want to share the insight into why it does work!

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One Change at a Time

One of the Agile Manifesto’s twelve principles states,  “At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly.” Many Agile teams hold biweekly retrospectives that result in concrete actions executed in the next time period (sprint). There are many types of tuning and adjustments that a team can do, such as improve the workflow, automate tasks, and increase team cooperation.

Is it a good habit for retrospectives to focus on the same type of improvement, or should the team alter the type of improvements? In this blog, I look into the effect of multiple consecutive actions that affect the flow of work.

The simulation is inspired by the getKanban Board Game, a physical game designed to teach the concepts and mechanics of Kanban for software development in a class or workshop setting.

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Organisational Inertia – A Predictor for Success of Agile Transformations? (Part 2)

In part 1 (Organisational Inertia – Part 1) I have focussed on the question: ‘Organisational Inertia – What is it?’. This blog addresses the question ‘How do we measure it?’.

I’ll start from the definition of Organisational Inertia as defined in part 1. Then connect to existing models of Organisational Inertia and the relation to Agile teams and show how the analog with Physics is used to find a measure for the ‘acceleration’. Then I’ll combine these elements to provide a way of measuring inertia. Finally I’ll provide basic examples.

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Phoney Deadlines are Deadly for Achieving Results

Ever had deadlines that must be met causing short-term decisions to be made? Ever worked over time with your team to meet an important deadline after which the delivered product wasn’t used for a couple of weeks?

I believe we all know these examples where deadlines are imposed on the team for questionable reasons.

Yet, deadlines are part of reality and we have to deal with them. Certainly, there is business value in meeting them but they also have costs.Read more →

Organisational Inertia – A Predictor for Successful Agile Transformations? (Part 1)

Organisational Gravity is well-known from the book ‘Succeeding with Agile’ by Mike Cohn and described therein as ‘the forces that pull an organisation back into old habits’ [Coh09].

When an organization makes a transition towards an Agile Organisation and the necessary changes happen at a too slow rate eventually Organisational Gravity will pull the organisation back into where it was before the transition attempt. The well-known property, see e.g., of an organisation that is associated with how fast the organisation can change on change-triggering events is known as Organisational Inertia. If it is too high it takes a lot of energy to make changes happen and the Organisational Gravity may make it impossible to complete the organisational transition.

Can we use Organisational Inertia as a predictor for how long it will take (on average) for the Agile transition to complete?

Other questions that come to mind are: What is ‘Organisational Inertia’? How do we measure it? What does it tell us? How does it help? Can we change it? And how?
This blog will address the first question: ‘What is it?’.

In next blogs I will focus on how Agile teams affect the organisation’s inertia and how to measure it.

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Group Conformity & Velocity in Scrum

A team that I have coached took up and voluntarily committed to a sprint backlog that was not realistic. Worse, not only it was clear afterwards that it was unrealistic, but they knew already right from the start of the sprint. How come they committed to a sprint backlog while they already knew it was not realistic?
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