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| Criterion |  Knowledge Modeling PARA Method |  Knowledge Modeling Domain Storytelling |  Delivery Monte Carlo Forecasting |  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. | Domain Storytelling translates domain knowledge into a shared narrative of actors, objects, and workflows. It is especially useful when language, rules, and process understanding must be tightly connected. | 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 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 | Low | High | Medium |
Timedifferent | 1-3 h Setup, laufend | 1-2 h | 30-90 min Setup, danach laufend | Ongoing |
Participantsdifferent | 1 | 2-8 | 1-8 | 2-12 |
Formatdifferent | Async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | PARA Structure, Project Notes, Knowledge Archive | Domain Story, Glossary Notes, Process Narrative | Forecast Percentiles, Throughput Dataset, Risk Communication | Kanban board, WIP policies, Flow metrics |
Tagsno overlap | KnowledgeProductivityOrganization | KnowledgeDomain-Driven DesignProcess | ForecastingFlowDelivery | FlowVisual managementDelivery |