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Criterion
Paper illustration of Shape Up with its method-specific working model.
Delivery
Shape Up
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Dot Estimation.
Facilitation
Dot Estimation
Paper illustration for NoEstimates.
Agile
NoEstimates
Purposedifferent
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.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 size or effort can only be estimated roughly, Dot Estimation condenses the group's experience into a quick range. It turns individual contributions into a visible selection. The result is captured as an Effort Heatmap, Risk Signals, and Discussion Targets.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
MediumHighLowMedium
Timedifferent
6 Wochen Cycle + 2 Wochen Cool-Down30-90 min Setup, danach laufend5-20 minlaufend
Participantsdifferent
2-4 pro Pitch1-83-202-12
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshopWorkshop + async
Outputdifferent
Pitches, Bet-table decisions, Hill charts, Cooldown outcomesForecast Percentiles, Throughput Dataset, Risk CommunicationEffort Heatmap, Risk Signals, Discussion TargetsThroughput Data, Flow Forecast, Slicing Rules
Tagsno overlap
PlanningDeliveryAutonomy
ForecastingFlowDelivery
EstimationEffortRisk
EstimationForecastingFlow
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