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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration of Lean Startup with its method-specific working model.
Innovation
Lean Startup
Paper illustration of a four-column Kanban board with limited ongoing work, a visible blocker and a review loop.
Engineering
Kanban
Paper illustration for NoEstimates.
Agile
NoEstimates
Purposedifferent
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.For uncertain business assumptions, the method forces the idea into contact with real market reactions early. It separates wishful picture, assumption, and observable behavior, so that learning becomes faster than planning. This translates uncertainty into measurable insight.When work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable.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.
Complexitydifferent
HighMediumMediumMedium
Timedifferent
30-90 min Setup, danach laufendWochen bis Monate je LernzyklusOngoinglaufend
Participantsdifferent
1-82-82-122-12
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationHypothesis list, MVPs, Learning reports, Pivot or persevere decisionKanban board, WIP policies, Flow metricsThroughput Data, Flow Forecast, Slicing Rules
Tagsno overlap
ForecastingFlowDelivery
LeanStartupValidationMVP
FlowVisual managementDelivery
EstimationForecastingFlow
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