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Criterion
Value Stream Mapping workspace showing the question, observations, and next decision.
Delivery
Value Stream Mapping
Paper illustration of Scrum with its method-specific working model.
Agile
Scrum
Story Splitting method illustration showing its working structure
Agile
Story Splitting
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
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.When a team needs to work in a fixed delivery rhythm, it stabilizes cadence, responsibility, and feedback loops. It makes collaboration reliable across sprint boundaries.When a story becomes too large for a clean flow, it breaks scope down along value and risk. It shapes the work into a form that ships earlier and is easier to verify.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
MediumMediumMediumHigh
Timedifferent
1-3 h1-4 Wochen je Sprint, laufend30-60 min30-90 min Setup, danach laufend
Participantsdifferent
4-103-10 (Scrum Team)2-61-8
Formatdifferent
WorkshopWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Current-state map, Future-state map, Bottleneck listProduct Backlog, Sprint Backlog, Increment, Sprint GoalSmaller Stories, Acceptance Criteria, Split RationaleForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
LeanFlowWasteDelivery
AgileIterationCadence
BacklogIterationDelivery
ForecastingFlowDelivery
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