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| Criterion |  Agile Affinity Estimation |  Agile NoEstimates |  Agile Planning Poker |  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 estimates within the team diverge too much, it puts differing expectations on a common test bench. It separates rough complexity from unspoken assumptions. | 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 | 2-5 min je Item | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-12 | 2-12 | 3-9 | 1-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Throughput Data, Flow Forecast, Slicing Rules | Relative Estimates, Assumption Notes, Split Candidates | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationBacklogRelative sizing | EstimationForecastingFlow | EstimationAgileRelative sizingTeam | ForecastingFlowDelivery |