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| Criterion |  Knowledge Modeling PARA Method |  Agile NoEstimates |  Agile Planning Poker |  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. | 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 | 1-3 h Setup, laufend | laufend | 2-5 min je Item | 30-90 min Setup, danach laufend |
Participantsdifferent | 1 | 2-12 | 3-9 | 1-8 |
Formatdifferent | Async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PARA Structure, Project Notes, Knowledge Archive | Throughput Data, Flow Forecast, Slicing Rules | Relative Estimates, Assumption Notes, Split Candidates | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | KnowledgeProductivityOrganization | EstimationForecastingFlow | EstimationAgileRelative sizingTeam | ForecastingFlowDelivery |