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Criterion
Paper illustration for Ideal Days.
Agile
Ideal Days
Paper illustration for Planning Poker.
Agile
Planning Poker
Paper illustration for NoEstimates.
Agile
NoEstimates
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 estimates within the team diverge too much, it puts differing expectations on a common test bench. It separates rough complexity from unspoken assumptions.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.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
LowLowMediumHigh
Timedifferent
15-60 min2-5 min je Itemlaufend30-90 min Setup, danach laufend
Participantsdifferent
2-93-92-121-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Ideal Day Estimates, Assumption Notes, Capacity CaveatsRelative Estimates, Assumption Notes, Split CandidatesThroughput Data, Flow Forecast, Slicing RulesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationEffortAgile
EstimationAgileRelative sizingTeam
EstimationForecastingFlow
ForecastingFlowDelivery
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