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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration of Shape Up with its method-specific working model.
Delivery
Shape Up
Paper illustration for PERT Estimation.
Decision Making
PERT Estimation
Paper illustration for NoEstimates.
Agile
NoEstimates
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.Shape Up helps clarify scope, sequence, and delivery flow. It makes work boundaries and decisions explicit and captures results as pitches, bet-table decisions, and hill charts.On complex initiatives, a plain average tends to understate just how uncertain the outcome really is. It separates options, evaluation criteria, and open risks. The result is captured as a PERT Estimate, Expected Value, and Risk Notes.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.
Complexitydifferent
HighMediumMediumMedium
Timedifferent
30-90 min Setup, danach laufend6 Wochen Cycle + 2 Wochen Cool-Down15-45 minlaufend
Participantsdifferent
1-82-4 pro Pitch1-82-12
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationPitches, Bet-table decisions, Hill charts, Cooldown outcomesPERT Estimate, Expected Value, Risk NotesThroughput Data, Flow Forecast, Slicing Rules
Tagsno overlap
ForecastingFlowDelivery
PlanningDeliveryAutonomy
EstimationUncertaintyRisk
EstimationForecastingFlow
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