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Criterion
Paper illustration for Theory of Constraints.
Operations
Theory of Constraints
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
Purposedifferent
For a system that does not get faster despite effort, the method directs attention to the bottleneck. It concentrates improvement on the point that actually limits throughput.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.
Complexitydifferent
HighMediumLowHigh
Timedifferent
2-4 h Analyse, laufendlaufend20-45 min30-90 min Setup, danach laufend
Participantsdifferent
3-122-123-121-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Constraint Map, Improvement Plan, Flow MetricsThroughput Data, Flow Forecast, Slicing RulesLessons learned, Action items, Event summaryForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
OperationsConstraintsFlowImprovement
EstimationForecastingFlow
LearningOperationsImprovement
ForecastingFlowDelivery
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