methodatlas
Compare

View methods side by side.

Choose up to four methods. Add them using the search and share the comparison by copying its link.

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 NoEstimates.
Agile
NoEstimates
Paper illustration for Three-Point Estimation.
Decision Making
Three-Point Estimation
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 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.A single estimate for effort, time, or risk often looks too smooth for the real uncertainty behind it. It separates options, evaluation criteria, and open risks. The result is captured as a Three-Point Estimate, Risk Range, and Assumption Notes.
Complexitydifferent
MediumHighMediumMedium
Timedifferent
6 Wochen Cycle + 2 Wochen Cool-Down30-90 min Setup, danach laufendlaufend10-30 min je Item
Participantsdifferent
2-4 pro Pitch1-82-121-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Pitches, Bet-table decisions, Hill charts, Cooldown outcomesForecast Percentiles, Throughput Dataset, Risk CommunicationThroughput Data, Flow Forecast, Slicing RulesThree-Point Estimate, Risk Range, Assumption Notes
Tagsno overlap
PlanningDeliveryAutonomy
ForecastingFlowDelivery
EstimationForecastingFlow
EstimationUncertaintyForecasting
Add more methods