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Criterion
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration of a review with planned work, actual event sequence, comparison, and assigned improvement actions.
Operations
After-Action Review
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Learning Review.
Operations
Learning Review
Purposedifferent
When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules.After missions or project phases with unclear outcomes, the method makes visible what actually happened and what can be learned from it. It separates course, effect, and causes so experience turns into solid improvement.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.After a project phase with mixed results, the method makes learning from the individual case reusable. It connects events, decisions, and systemic conditions into robust insights.
Complexitydifferent
MediumLowHighMedium
Timedifferent
laufend20-45 min30-90 min Setup, danach laufend1-3 h
Participantsdifferent
2-123-121-83-10
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop
Outputdifferent
Throughput Data, Flow Forecast, Slicing RulesLessons learned, Action items, Event summaryForecast Percentiles, Throughput Dataset, Risk CommunicationLearning Review Notes, System Factors, Improvement Actions
Tagsno overlap
EstimationForecastingFlow
LearningOperationsImprovement
ForecastingFlowDelivery
LearningRetrospectiveIncidentOperations
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