I was fortunate to hear a talk recently by Martin Aspeli, Head of Engineering at Deloitte Digital, explaining how and why to use metrics to predict software delivery rather than estimates. Here are my brief notes:
tl;dr: there is no need to spend time estimating — with discipline and data we can forecast!
- Predictability is more important than pure top speed for most organisations.
- Humans are notoriously bad at estimating. This is a facet of human nature — as a species we are bad at estimating. See Hofstadter’s Law.
- We tend to ignore Wait Time when estimating, yet Wait Time makes up the majority of Cycle Time in most organisations doing knowledge work like software development.
- With some discipline in how we use Jira, we can start to use metrics to help track how long things actually take — this increases predictability.
- We need to take care to ensure that the Arrival Rate of new work items does not exceed the Departure Rate. Treat our team and its context as a system to be cared for.
- Use a Cumulative Flow Diagram (CFD) to track the system health.
- Measure end-to-end Cycle Time of actual stories. No need to size the stories with Story Points or t-shirt sizes. The ensemble of stories provides confidence measures.
- As people are “loaded” beyond about 80% capacity, their capacity to respond drops dramatically. This is basic queueing theory.
- Forecast the delivery dates for batches of work using Monte Carlo simulations. This gives confidence measures (50%, 85%, 95%, etc.) allowing stakeholders to choose dates based on likelihood and risk.
- Thinking, Fast and Slow — Daniel Kahneman
- Essential Kanban, Condensed — David J. Anderson & Andy Carmichael
- Actionable Agile Metrics for Predictability — Daniel S. Vacanti
- The Principles of Product Development Flow — Don Reinertsen
- Actionable Agile: http://actionableagile.com(tools for working with data from JIRA and similar systems)
- Focused Objective: https://github.com/FocusedObjective/FocusedObjective.Resources (open source Excel based tools)