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| Criterion |  Decision Making PERT Estimation |  Agile NoEstimates |  Knowledge Modeling Knowledge Mapping |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | On complex initiatives, a plain average tends to understate just how uncertain the outcome really is. It separates options, evaluation criteria, and open risks. The result is captured as a PERT Estimate, Expected Value, and Risk Notes. | 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. | Knowledge Mapping makes knowledge, gaps, and transfer paths visible across a field. It fits when expertise should not just exist but also be findable and transferable. | 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 | Medium | High |
Timedifferent | 15-45 min | laufend | 1-3 h | 30-90 min Setup, danach laufend |
Participantsdifferent | 1-8 | 2-12 | 3-12 | 1-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | PERT Estimate, Expected Value, Risk Notes | Throughput Data, Flow Forecast, Slicing Rules | Knowledge Map, Critical Knowledge Areas, Transfer Plan | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationUncertaintyRisk | EstimationForecastingFlow | KnowledgeMappingRisk | ForecastingFlowDelivery |