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| Criterion |  Knowledge Modeling PARA Method |  Delivery Monte Carlo Forecasting |  Facilitation Dot Estimation |
|---|---|---|---|
Purposedifferent | The PARA Method organizes knowledge into Projects, Areas, Resources, and Archives. It creates order when digital notes, files, and tasks no longer cleanly fit current work. | 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 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. |
Complexitydifferent | Medium | High | Low |
Timedifferent | 1-3 h Setup, laufend | 30-90 min Setup, danach laufend | 5-20 min |
Participantsdifferent | 1 | 1-8 | 3-20 |
Formatdifferent | Async | Workshop + async | Workshop |
Outputdifferent | PARA Structure, Project Notes, Knowledge Archive | Forecast Percentiles, Throughput Dataset, Risk Communication | Effort Heatmap, Risk Signals, Discussion Targets |
Tagsno overlap | KnowledgeProductivityOrganization | ForecastingFlowDelivery | EstimationEffortRisk |
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