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 Teach-back.
Knowledge Modeling
Teach-back
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Feynman Technique.
Knowledge Modeling
Feynman Technique
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.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.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
LowHighLowMedium
Timedifferent
5-20 min30-90 min Setup, danach laufend20-60 min1-3 h
Participantsdifferent
2-81-814-10
Formatdifferent
Workshop + asyncWorkshop + asyncAsyncWorkshop
Outputdifferent
Understanding Check, Clarification Notes, Learning GapsForecast Percentiles, Throughput Dataset, Risk CommunicationPlain-language Explanation, Knowledge Gaps, Study NotesCurrent-state map, Future-state map, Bottleneck list
Tagsno overlap
LearningUnderstandingCommunication
ForecastingFlowDelivery
LearningKnowledgeUnderstanding
LeanFlowWasteDelivery
Add more methods