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| Criterion |  Knowledge Modeling PARA Method |  Agile NoEstimates |  Decision Making Wideband Delphi |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
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. | 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. | A single opinion rarely holds up for planning when the future is genuinely uncertain. It separates options, evaluation criteria, and open risks. The result is captured as an Estimate Range, Assumption Log, and Expert Consensus Notes. | 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 | High | High |
Timedifferent | 1-3 h Setup, laufend | laufend | 1-4 h or multiple rounds | 30-90 min Setup, danach laufend |
Participantsdifferent | 1 | 2-12 | 4-12 Experten | 1-8 |
Formatdifferent | Async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PARA Structure, Project Notes, Knowledge Archive | Throughput Data, Flow Forecast, Slicing Rules | Estimate Range, Assumption Log, Expert Consensus Notes | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | KnowledgeProductivityOrganization | EstimationForecastingFlow | EstimationExpertsForecasting | ForecastingFlowDelivery |