The theme song of adaptive development teams isn't "Qué Será, Será" - planning delivery like you mean it.

Despite our best efforts the practices of iterative development seem no better at answering the age-old question of business sponsors: "When am I going to get it?"

And we seem to be making the problem worse by insisting on talking in language that means nothing to people outside the team.

This interactive discussion will challenge current thinking about story sizing and planning, tracking progress and forecasting delivery.

We'll be leaving the fantasy deterministic world and visiting our scary probabilistic reality. For the Matrix fans among us it'll be a red pill/blue pill moment...but don't worry where we'll be going they language is easily understood.



Outline/Structure of the Tutorial

Session Plan:
Together we'll explore current experiences with forecasting practices and gaps in expectations that typically arise.
We'll look at how lean/agile planning techniques could take better advantage of easily available data.
We'll have a quick demonstration of data collection techniques.
Finally we'll (in teams) build charts that can be immediately applicable to improving product delivery forecast accuracy.


Session Type: Lecture 60%, hands-on 25%, demo 15%,


Learning Outcome

Folks will leave this session knowing how to better assess the capacity of their team(s) and make more reliable delivery promises.

They will be able to use probability to explain and negotiate delivery expectations and track team capacity.

Target Audience

Executives, Product Owners, Development Managers, Scrum Masters

schedule Submitted 4 years ago

Public Feedback

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  • Dag Rowe
    By Dag Rowe  ~  4 years ago
    reply Reply

    Andrew, what lean agile planning techniques are you planning to examine?

    • Andrew Annett
      By Andrew Annett  ~  4 years ago
      reply Reply


      This session draws on my experience at D2L and offers ideas and practices to do more realistic release planning. Typically teams are ok with iteration planning but lose the plot (or confidence) when considering a longer term view. This, of course is a major source of contention with funders.

      My first goal is to illustrate the value of capturing team flow performance data in the simplest way possible.

      Using story cycle time serves as the basis to understand the distribution of a teams delivery - this is best described by the work of Alexei Zieglov, Troy Magennis and  Dan Vacanti and the use of the Wiebull distribution to support probability-based planning.

      Lastly, the use of Random Branch Sampling (thanks to Dimitar Bakardhziev), is introduced to show ways of addressing larger-scale planning with lightweight approaches.

      Is this helpful?