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| Criterion |  Delivery Monte Carlo Forecasting |  Operations DMAIC |  Operations Root Cause Analysis |  Operations Root Cause Tree 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. | For a process problem with fluctuating performance, the method brings analysis and improvement into a disciplined sequence. It creates a framework in which numbers, causes, and control come together. | 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. | For a problem with several suspected causes, the method builds a causal structure instead of a mere list. It makes visible how causes connect and where the strongest points of leverage lie. |
Complexitydifferent | High | High | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 2-12 Wochen | 1-4 h | 1-3 h |
Participantsdifferent | 1-8 | 3-10 | 3-8 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Project Charter, Measurement Plan, Cause Analysis, Control Plan | Problem Statement, Cause Hypotheses, Confirmed Causes, Action Plan | Cause Tree, Evidence Notes, Countermeasures |
Tagsno overlap | ForecastingFlowDelivery | Continuous improvementQualityProcess improvement | Root causeProblem solvingQualityIncident | Root causeTreeIncidentQuality |