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| Criterion |  Operations PDCA Cycle |  Agile NoEstimates |  Decision Making Force Field Analysis |  Product Strategy DIBB |
|---|---|---|---|---|
Purposedifferent | For an improvement that still has to prove itself in everyday work, the method runs it through small learning loops. It connects planning, checking, and standardization into a repeatable learning mode. | 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. | In change initiatives, supporting and restraining forces sit at the table at the same time. Force Field Analysis makes these tensions explicit and shows where change can be pushed forward by strengthening or relieving. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. |
Complexitydifferent | Low | Medium | Low | Low |
Timedifferent | 1 h bis mehrere Wochen | laufend | 45-90 min | 1-2 h |
Participantsdifferent | 1-8 | 2-12 | 3-12 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Throughput Data, Flow Forecast, Slicing Rules | Force Field Map, Change Levers, Risk Notes | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | Continuous improvementLeanExperiments | EstimationForecastingFlow | ChangeDecisionStrategy | StrategyDecisionAssumptionsHypothesis |