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| Criterion |  Agile Affinity Estimation |  Agile NoEstimates |  Facilitation Dot Estimation |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | 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. | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | When size or effort can only be estimated roughly, Dot Estimation condenses the group's experience into a quick range. It turns individual contributions into a visible selection. The result is captured as an Effort Heatmap, Risk Signals, and Discussion Targets. | 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. |
Complexitydifferent | Medium | Medium | Low | High |
Timedifferent | 30-90 min | laufend | 5-20 min | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-12 | 2-12 | 3-20 | 1-8 |
Formatdifferent | Workshop | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Throughput Data, Flow Forecast, Slicing Rules | Effort Heatmap, Risk Signals, Discussion Targets | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationBacklogRelative sizing | EstimationForecastingFlow | EstimationEffortRisk | ForecastingFlowDelivery |