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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Fault Tree Analysis |  Agile Ideal Days |
|---|---|---|---|
Purposedifferent | 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. | For a critical top event with several possible triggers, the method logically models failure paths. It makes visible which combinations of conditions can lead to damage. | 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. |
Complexitydifferent | High | High | Low |
Timedifferent | 30-90 min Setup, danach laufend | 2-6 h | 15-60 min |
Participantsdifferent | 1-8 | 3-8 | 2-9 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Ideal Day Estimates, Assumption Notes, Capacity Caveats |
Tagsno overlap | ForecastingFlowDelivery | RiskRoot causeSafety | EstimationEffortAgile |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
