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Criterion
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration of Shape Up with its method-specific working model.
Delivery
Shape Up
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
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.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.
Complexitydifferent
MediumMediumHigh
Timedifferent
laufend6 Wochen Cycle + 2 Wochen Cool-Down30-90 min Setup, danach laufend
Participantsdifferent
2-122-4 pro Pitch1-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Throughput Data, Flow Forecast, Slicing RulesPitches, Bet-table decisions, Hill charts, Cooldown outcomesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationForecastingFlow
PlanningDeliveryAutonomy
ForecastingFlowDelivery
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Often compared together

Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration for Communication Plan
Delivery
Communication Plan
Paper illustration for Three-Point Estimation.
Decision Making
Three-Point Estimation
Value Stream Mapping workspace showing the question, observations, and next decision.
Delivery
Value Stream Mapping