View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Delivery Monte Carlo Forecasting |  Engineering Kanban |  Agile Planning Poker |  Agile Affinity Estimation |
|---|---|---|---|---|
Purposedifferent | Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication. | When work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable. | When estimates within the team diverge too much, it puts differing expectations on a common test bench. It separates rough complexity from unspoken assumptions. | When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes. |
Complexitydifferent | High | Medium | Low | Medium |
Timedifferent | 30-90 min Setup, danach laufend | Ongoing | 2-5 min je Item | 30-90 min |
Participantsdifferent | 1-8 | 2-12 | 3-9 | 3-12 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Kanban board, WIP policies, Flow metrics | Relative Estimates, Assumption Notes, Split Candidates | Affinity Size Map, Grouped Estimates, Unclear Items |
Tagsno overlap | ForecastingFlowDelivery | FlowVisual managementDelivery | EstimationAgileRelative sizingTeam | EstimationBacklogRelative sizing |