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Criterion
Paper illustration of Scrum with its method-specific working model.
Agile
Scrum
Paper illustration for Dot Estimation.
Facilitation
Dot Estimation
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
When a team needs to work in a fixed delivery rhythm, it stabilizes cadence, responsibility, and feedback loops. It makes collaboration reliable across sprint boundaries.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.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
MediumLowHigh
Timedifferent
1-4 Wochen je Sprint, laufend5-20 min30-90 min Setup, danach laufend
Participantsdifferent
3-10 (Scrum Team)3-201-8
Formatdifferent
Workshop + asyncWorkshopWorkshop + async
Outputdifferent
Product Backlog, Sprint Backlog, Increment, Sprint GoalEffort Heatmap, Risk Signals, Discussion TargetsForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
AgileIterationCadence
EstimationEffortRisk
ForecastingFlowDelivery
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Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration of Dual Track Agile with its method-specific working model.
Product Discovery
Dual-Track Agile