Predictable Delivery
summary
Argues that past performance predicts future delivery and shows how to turn JIRA data into dates using velocity, cycle time and throughput rather than fuzzy promises. Demonstrates cumulative flow diagram and burn-down patterns to eyeball that a team doing ~2 stories/week will need ~6 months to finish current work, explains converting work into t-shirt sizes then to time, and prescribes a buffer for emergent requirements (historical ratios: e.g., 25 planned → ~3 emergent), plus tracking categories to hold scope.
AI-written summary · about
chapters (17)
- 0:00Introduction to predictable delivery
- 1:11Role of product managers in estimating
- 6:16Benefits of estimating and forecasting
- 9:50Who should estimate and how
- 12:02Team signatures and predictability
- 13:31Burn down charts and team metrics
- 17:53T-shirt sizing for estimation
- 19:24Buffering for emergent requirements
- 25:21Tracking and minimal data requirements
- 28:16Maturity of product and team
- 30:54Decomposing large initiatives
- 33:02Grooming and lead time management
- 34:53Key lessons and team dynamics
- 37:15Managing scope for deadlines
- 40:30Design and development estimation
- 41:21Handling uncertainty in estimates
- 43:12Q&A and closing remarks
related talks
44mPredictably Agile
16mPractically predictable – Forecasting delivery when nothing is certai
46mEstimates and complexity: a tale in 42 and a half minutes
36mThe Development Metrics You Should Use (but Don’t)
44mAgile metrics for predicting the future
05mPlanning Theater: The Play That's Killing Your Delivery
41mThe Development Metrics You Should Use (but Don’t)
57mThe Science of not estimating
