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Criterion
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Paper illustration of the Force Field Analysis working structure.
Decision Making
Force Field Analysis
Purposedifferent
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.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.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.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.
Complexitydifferent
MediumHighLowLow
Timedifferent
laufend30-90 min Setup, danach laufend1-2 h45-90 min
Participantsdifferent
2-121-82-83-12
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop
Outputdifferent
Throughput Data, Flow Forecast, Slicing RulesForecast Percentiles, Throughput Dataset, Risk CommunicationDIBB document, Belief list, Bet list, Learning reportForce Field Map, Change Levers, Risk Notes
Tagsno overlap
EstimationForecastingFlow
ForecastingFlowDelivery
StrategyDecisionAssumptionsHypothesis
ChangeDecisionStrategy
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