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Criterion
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Value Stream Mapping workspace showing the question, observations, and next decision.
Delivery
Value Stream Mapping
Purposedifferent
When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes.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 delivery work dissolves into wait times, handoffs, and hidden effort, Value Stream Mapping makes the actual flow visible. The method exposes bottlenecks, idle time, and feedback loops so lead time and improvement levers become tangible.
Complexitydifferent
MediumHighMedium
Timedifferent
30-90 min30-90 min Setup, danach laufend1-3 h
Participantsdifferent
3-121-84-10
Formatdifferent
WorkshopWorkshop + asyncWorkshop
Outputdifferent
Affinity Size Map, Grouped Estimates, Unclear ItemsForecast Percentiles, Throughput Dataset, Risk CommunicationCurrent-state map, Future-state map, Bottleneck list
Tagsno overlap
EstimationBacklogRelative sizing
ForecastingFlowDelivery
LeanFlowWasteDelivery
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