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Criterion
Paper illustration for Teach-back.
Knowledge Modeling
Teach-back
Value Stream Mapping workspace showing the question, observations, and next decision.
Delivery
Value Stream Mapping
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Feynman Technique.
Knowledge Modeling
Feynman Technique
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.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.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.The Feynman Technique checks understanding by explaining a topic so that it stays easy for others to understand. It makes knowledge gaps visible before they become costly in everyday work.
Complexitydifferent
LowMediumHighLow
Timedifferent
5-20 min1-3 h30-90 min Setup, danach laufend20-60 min
Participantsdifferent
2-84-101-81
Formatdifferent
Workshop + asyncWorkshopWorkshop + asyncAsync
Outputdifferent
Understanding Check, Clarification Notes, Learning GapsCurrent-state map, Future-state map, Bottleneck listForecast Percentiles, Throughput Dataset, Risk CommunicationPlain-language Explanation, Knowledge Gaps, Study Notes
Tagsno overlap
LearningUnderstandingCommunication
LeanFlowWasteDelivery
ForecastingFlowDelivery
LearningKnowledgeUnderstanding
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