Drunk Agile
Episode 43 - Monte Carlo With Unkonwn Backlog Size
Mar 28, 2022
Agile expert Nisha, product management specialist Dan, and software development guru Prateek explore using Monte Carlo simulation to forecast completion times for products when backlog size is unknown. They discuss utilizing the simulation for project estimation with uncertain work item counts, managing uncertainty in backlog items, and forecasting future work in Agile projects through historical data analysis and simulations.
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Quick takeaways
- Forecasting with uncertain backlogs requires Monte Carlo simulation with ranges for accurate estimation.
- Continuous forecasting with Monte Carlo simulations enables dynamic project planning and adaptability to changing circumstances.
Deep dives
Challenges in Monte Carlo Simulation Forecasting
When the exact number of items in a backlog is unknown, running a Monte Carlo simulation for forecasting becomes a challenge. Typically, backlogs do not have exact item counts, making it hard to predict project completion dates. The podcast discussed an example where the MVP had a known item count, but the additional features were uncertain, requiring a range for estimation. This uncertainty highlights the importance of realistic forecasting methods that accommodate variability in project scopes.
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