Posted by in Featured, Forecasting, Reference |

Earlier this month, Arne Rooke from interviewed Troy Magennis on forecasting in the lead-up to the LKCE conference later this year. You can read the full interview here:


At the Lean/Kanban conference in Chicago earlier this year, Troy Magennis was awarded with the Brickell Key Award for his work with Monte Carlo Simulations combined with Lean/Kanban. The announcement said that his work will „change the way our industry will work“. I didn’t know Troy very well in Chicago, but I had some opportunities to catch up with him. And it became clearer and clearer how right this statement is. His work will change the way our industry works – or at least big parts of it. The use of Monte Carlo Simulations can have a big impact on predictability, planning and accounting. And it might release teams from endless estimation meetings with often are a big pain. And besides this: Troy is a really nice guy. Two good reasons to not only invite him to Lean Kanban Central Europe but also to do an interview with him. In this interview we talk about:

  • Cycle Time Forecasting and Predictability
  • Story Points
  • Brook’s Law
  • Kanban Tools

Download the full interview here

P.S. Troy will be giving a presentation and a Tutorial on Lean Forecasting at Lean Kanban Central Europe 2013

Thank you Arne for the support.

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Liquidity and Pull Transactions

Posted by in Announcements, Featured, Reference, Tools |

David Anderson has been presenting his thoughts on using the number of card pulls (Pull Transactions) as a metric for measuring the health and liquidity of a Kanban process. He starts the conversation here –

Our software has always charted the number of empty positions and queued positions over time which is a close proxy to this measurement and found the sum of Empty and Queued each time period a reliable indicator for forecasting where the stress points are in the Kanban board process. We decided that this liquidity chart might add an easier dimension for people to understand the impact of changes when they are experimenting with our simulation software. So we added the chart.









We are still refining the way it looks and going to add trend lines to make it easier to discern the running average, and we are also building a set of examples that show how it adds value. Remember -this is the ONLY chart where higher is BETTER. All of our other measure are lower is better – causing us some stress internally!

This is in the latest version (v1.3.1)  you can download now.


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New Video – Managing Risk

Posted by in Featured, Forecasting, Reference |

Managing software project risk is an area we are seeing great need in our industry. At the recent Lean Software and Systems conference, and at Agile 2012 later this year, we demonstrate that software delivery estimates aren’t a single date, but rather a multi-modal probability distribution curve. We put together this 1-minute introduction to the topic. See it on our Video page or YouTube –

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Intro video: Probabilistic approaches to estimation

Posted by in Featured, Reference |

Troy Magennis spoke at the SoCal Lean group: on the basic concepts of Monte-carlo simulation of software projects. It was captured on video by Pascal Pinck (from and is a great starting point on the topic of probabilistic approaches to estimation.


“The SoCal Lean/Kanban meetup had another excellent speaker this month in the form of Seattle-based software exec and lean thinker Troy Magennis. Troy has studied probabilistic approaches to estimation and offers a number of extremely valuable insights and shortcuts to get better estimates and have more useful conversations with stakeholders while incurring less work.”

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Whitepaper: Introduction to Monte-carlo Simulation in Software Development

Posted by in Featured, Reference |

We have just completed the first of many whitepapers on technology expertise we wish to share. This whitepaper is on Monte-carlo simulation and its application to more reliable estimation and management of software development projects. Feel free to read and share this publication with your teams and management.

Introduction to Monte-carlo Simulation for Software Development – Forecasting and managing software development project risks & uncertainty

Monte-carlo analysis is the tool of choice for managing risk in many fields where risk is an inherent part of doing business. This paper examines how to use monte-carlo techniques to understand and leverage risk in Software Development projects and teams.

For software development, it is often necessary to estimate a project upfront in order to get project approval, obtain budget and hire the correct team size and skill-mix. This is often at odds with the Agile development methodology where full upfront design and specification is avoided, and delivery happens in small iterations until a backlog is completed. The desire to work iteration to iteration and choose a finite level of work each cycle is compelling, and it does un-deniably bring value to production earlier than a pure waterfall approach.

However, the fact still remains that in order to provide any value to an organization, a finite minimum level of functionality (work) needs to be delivered by a preferred date, within a budget constraint; very few companies will sign off on a project that has no target date, and an open budget. Often delays incur high cost; not just development costs, but also as competitors launch new feature first, or take an increasing market share. Even with Agile teams it is important for any development manager or organization to be ready to answer the following questions on an ongoing basis –

1.    How much will this product cost to develop and deliver?
2.    What is the likelihood of releasing by date x?
3.    What resources do you need to hit date x (money equals people, so the question is often how much more money do you need to hit date x)?

This paper introduces a technique for answering these questions given the risks involved in software development and delivery. Monte-carlo analysis is a proven technique for determining the likelihood of an outcome in the face of many difficult to measure input criteria. Monte-carlo analysis doesn’t completely eliminate any risk, but it does give a much higher degree of satisfactory answer than the plain guesses and gut feel that is employed today (as to release date) in many software projects.


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Kanban Simulator Demo – Short Video

Posted by in Reference |

This video shows the basic features of the Kanban Simulator we will be releasing for Beta shortly. We will be actively looking for companies and people willing to beta trial this tool, and if you are interested, please contact us for more information or email me directly:
(Note: This video has no sound; Workplace safe!)

If you have any comments, please feel free to email me:
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