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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Dual-Track Agile |  Operations 5 Whys |  Operations Root Cause Analysis |
|---|---|---|---|---|
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. | 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. | For a single, hard-to-explain deviation, the method exposes the causal chain behind the visible symptom. It keeps the cause open until a controllable condition emerges instead of a mere description. | When a problem keeps recurring and the cause stays unclear, the method exposes the underlying mechanism. It separates symptom, guess, and robust explanation from one another. |
Complexitydifferent | High | Medium | Low | Medium |
Timedifferent | 30-90 min Setup, danach laufend | Laufend, Wochen bis Monate | 15-30 min | 1-4 h |
Participantsdifferent | 1-8 | 4-10 | 2-6 | 3-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Root cause notes, Countermeasures | Problem Statement, Cause Hypotheses, Confirmed Causes, Action Plan |
Tagsno overlap | ForecastingFlowDelivery | AgileDiscoveryDelivery | Root causeIncidentLeanProblem solving | Root causeProblem solvingQualityIncident |