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Criterion
Paper illustration for Teach-back.
Knowledge Modeling
Teach-back
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
Teach-back checks whether an explanation actually got through to the other person. The method makes misunderstandings visible early and strengthens shared understanding in advisory and learning situations.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
LowHighMedium
Timedifferent
5-20 min30-90 min Setup, danach laufend1-3 h
Participantsdifferent
2-81-84-10
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop
Outputdifferent
Understanding Check, Clarification Notes, Learning GapsForecast Percentiles, Throughput Dataset, Risk CommunicationCurrent-state map, Future-state map, Bottleneck list
Tagsno overlap
LearningUnderstandingCommunication
ForecastingFlowDelivery
LeanFlowWasteDelivery
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Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration for Communication Plan
Delivery
Communication Plan
Paper illustration for Feynman Technique.
Knowledge Modeling
Feynman Technique