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| Criterion |  Knowledge Modeling PARA Method |  Agile Ideal Days |  Agile NoEstimates |  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 effort needs to be thought of as real working time, it offers a simple common denominator for comparison. It sorts work by value, risk, and delivery ability. The result is captured as Ideal Day Estimates, Assumption Notes, and Capacity Caveats. | 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. | 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 | Low | Medium | High |
Timedifferent | 1-3 h Setup, laufend | 15-60 min | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 1 | 2-9 | 2-12 | 1-8 |
Formatdifferent | Async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PARA Structure, Project Notes, Knowledge Archive | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | KnowledgeProductivityOrganization | EstimationEffortAgile | EstimationForecastingFlow | ForecastingFlowDelivery |