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| Criterion |  Operations Bottleneck Analysis |  Agile Ideal Days |  Product Discovery Dual-Track Agile |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | For a flow that backs up at one point, the method looks for the capacity limit with the greatest leverage. It explains why extra effort elsewhere barely improves throughput. | 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 uncertainty and delivery run in parallel, it separates learning work from implementation while keeping both connected. It prevents unvalidated ideas from falling directly into the delivery stream. | 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 | 15-60 min | Laufend, Wochen bis Monate | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-8 | 2-9 | 4-10 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | FlowMeasurementConstraints | EstimationEffortAgile | AgileDiscoveryDelivery | ForecastingFlowDelivery |