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| Criterion |  Knowledge Modeling PARA Method |  Delivery Monte Carlo Forecasting |  Agile NoEstimates |  Engineering Kanban |
|---|---|---|---|---|
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. | 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. | 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 work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable. |
Complexitydifferent | Medium | High | Medium | Medium |
Timedifferent | 1-3 h Setup, laufend | 30-90 min Setup, danach laufend | laufend | Ongoing |
Participantsdifferent | 1 | 1-8 | 2-12 | 2-12 |
Formatdifferent | Async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PARA Structure, Project Notes, Knowledge Archive | Forecast Percentiles, Throughput Dataset, Risk Communication | Throughput Data, Flow Forecast, Slicing Rules | Kanban board, WIP policies, Flow metrics |
Tagsno overlap | KnowledgeProductivityOrganization | ForecastingFlowDelivery | EstimationForecastingFlow | FlowVisual managementDelivery |