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Criterion
Blameless Postmortem workspace showing the question, observations, and next decision.
DevOps
Blameless Postmortem
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration for Future Reality Tree.
Systems Thinking
Future Reality Tree
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Purposedifferent
After an incident with damage or a near miss, the method creates a sober field for learning without assigning blame. It directs attention to the course of events, conditions, and effective countermeasures.When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together.A Future Reality Tree shows how desired actions are meant to lead to a better system state. The method checks whether a path of change holds together logically before time flows into implementation.DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report.
Complexitydifferent
MediumHighHighLow
Timedifferent
30-90 min1-4 Wochen2-4 h1-2 h
Participantsdifferent
3-126-30 Experten3-82-8
Formatdifferent
Workshop + asyncAsyncWorkshopWorkshop + async
Outputdifferent
Postmortem Doc, Action Items, TimelineExpert Forecast, Consensus Range, Assumption NotesFuture Reality Tree, Negative Branches, Assumption List, Improved InjectionsDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
Site Reliability EngineeringIncidentLearningReliability
ForecastingExpertsDecisionStrategy
Theory of ConstraintsSystems thinkingChange
StrategyDecisionAssumptionsHypothesis
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