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 for Ideal Days.
Agile
Ideal Days
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Wideband Delphi.
Decision Making
Wideband Delphi
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
When effort needs to be thought of as real working time, it offers a simple common denominator for comparison. It sorts work by value, risk, and delivery ability. The result is captured as Ideal Day Estimates, Assumption Notes, and Capacity Caveats.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 opinion rarely holds up for planning when the future is genuinely uncertain. It separates options, evaluation criteria, and open risks. The result is captured as an Estimate Range, Assumption Log, and Expert Consensus Notes.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
LowMediumHighHigh
Timedifferent
15-60 minlaufend1-4 h or multiple rounds30-90 min Setup, danach laufend
Participantsdifferent
2-92-124-12 Experten1-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Ideal Day Estimates, Assumption Notes, Capacity CaveatsThroughput Data, Flow Forecast, Slicing RulesEstimate Range, Assumption Log, Expert Consensus NotesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationEffortAgile
EstimationForecastingFlow
EstimationExpertsForecasting
ForecastingFlowDelivery
Add more methods