Abstract: This workshops teaches you how to capture data and use it for reliable project forecasting. Much has been written about what is possible to estimate and what is waste. This workshop shows a practical and simple (we are doing it with pen and paper) approach to forecasting without item effort estimation that can be used by anybody needing to answer the questions: How big? How long? and How likely?
Three estimation and forecasting scenarios will be practiced using pen, paper and dice -
- Reliably capturing and spotting errors in historical data
- 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)
By participating 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 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.
Attachments:
