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| Criterion |  Operations Bottleneck Analysis |  Delivery Monte Carlo Forecasting |  Agile Planning Poker |  Agile Affinity Estimation |
|---|---|---|---|---|
Purposedifferent | For a flow that backs up at one point, the method looks for the capacity limit with the greatest leverage. It explains why extra effort elsewhere barely improves throughput. | 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 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 | Medium | High | Low | Medium |
Timedifferent | 1-3 h | 30-90 min Setup, danach laufend | 2-5 min je Item | 30-90 min |
Participantsdifferent | 3-8 | 1-8 | 3-9 | 3-12 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Forecast Percentiles, Throughput Dataset, Risk Communication | Relative Estimates, Assumption Notes, Split Candidates | Affinity Size Map, Grouped Estimates, Unclear Items |
Tagsno overlap | FlowMeasurementConstraints | ForecastingFlowDelivery | EstimationAgileRelative sizingTeam | EstimationBacklogRelative sizing |