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Criterion
Paper illustration for Teach-back.
Knowledge Modeling
Teach-back
Paper illustration for Waste Analysis.
Operations
Waste Analysis
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
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.For a process that keeps people busy but creates little value, the method exposes waste. It directs attention to unnecessary movement, waiting times, rework, and overdelivery.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
LowLowHigh
Timedifferent
5-20 min45-120 min30-90 min Setup, danach laufend
Participantsdifferent
2-82-81-8
Formatdifferent
Workshop + asyncWorkshopWorkshop + async
Outputdifferent
Understanding Check, Clarification Notes, Learning GapsWaste Map, Prioritized Waste, Improvement BacklogForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
LearningUnderstandingCommunication
WasteLeanProcess improvement
ForecastingFlowDelivery
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Methods with strong topical overlap with the current selection, not yet in the comparison.

Value Stream Mapping workspace showing the question, observations, and next decision.
Delivery
Value Stream Mapping
Paper illustration for Communication Plan
Delivery
Communication Plan
Paper illustration for Feynman Technique.
Knowledge Modeling
Feynman Technique