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| Criterion |  Knowledge Modeling PARA Method |  Engineering Kanban |  Delivery Monte Carlo Forecasting |  Operations Bottleneck Analysis |
|---|---|---|---|---|
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 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. | 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 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. |
Complexitydifferent | Medium | Medium | High | Medium |
Timedifferent | 1-3 h Setup, laufend | Ongoing | 30-90 min Setup, danach laufend | 1-3 h |
Participantsdifferent | 1 | 2-12 | 1-8 | 3-8 |
Formatdifferent | Async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PARA Structure, Project Notes, Knowledge Archive | Kanban board, WIP policies, Flow metrics | Forecast Percentiles, Throughput Dataset, Risk Communication | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures |
Tagsno overlap | KnowledgeProductivityOrganization | FlowVisual managementDelivery | ForecastingFlowDelivery | FlowMeasurementConstraints |