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Monday, August 6 • 15:45 - 17:00
Probabilistic Forecasting using data: Measuring how long it's going to take to deliver (Adam Yuret) POPULAR

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Abstract:
"When will the project be done?!" This single question has created more dysfunction and psychological danger than probably any other single demand in history of work.
Story points, ideal hours, task hours, I’ve even seen a director who un-ironically tracks "developer minutes" to handle these issues.
Focused Objective has created some simple, easy to use (and free) forecasting tools which we can learn about while actually doing probabilistic forecasting using pen and paper.
Together we will workshop two scenarios:
Estimating total project size (story count) by sampling a subset of all features or epics
Forecasting completion date using probabilistic forecasting (Monte Carlo) of estimated or measured teams’ throughput (completion rate) or velocity (points)
In this workshop you will
Learn how much sample data is required to undertake a reliable forecast
Learn how to spot erroneous data or data that will mislead a forecast
Learn how to use story count estimates on a subset of features to forecast a projects combined total story count, or to see if the count you have been given is likely
Learn how to use historical data to perform a feature completion date forecast, or to see if the date you have been given is possible.
The processes described involve using dice to simulate uncertainty in projects and building a probabilistic picture of the more likely outcomes (often called Monte Carlo simulation). It is a fast and accurate way to combine historical data into meaningful and verifiable results. By performing a Monte Carlo forecast by hand, you will realize how easy the technique is to perform and not be afraid to use it in your next estimation or forecasting task.
We will also discuss the choice between estimation in points versus throughput and how this impacts forecasting accuracy. We will also discuss how most tools available for forecasting go wrong, and how to understand how accurate your forecast using these methods should be considered.

Learning Outcomes:
  • Learn how much sample data is required to undertake a reliable forecast
  • Learn how to use story count estimates on a subset of features to forecast a projects combined total story count, or to see if the count you have been given is likely
  • Learn how to use historical data to perform a feature completion date forecast, or to see if the date you have been given is possible.
  • Let’s bring real empirical data to the table and use it to make better decisions about how we manage our systems.


Speakers
avatar for Adam Yuret

Adam Yuret

Founder/Consultant, Context Driven Agility
Adam Yuret is an experienced systems thinker who has consulted small non-profits and fortune 100 clients on adopting context-driven systems to solving difficult problems. Adam started Context Driven Agility in 2010 to share his passion for humanistic flow-based systems full time... Read More →


Monday August 6, 2018 15:45 - 17:00
Marriott Salon 9