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Criterion
Four paper collections for projects, responsibilities, resources and archives.
Knowledge Modeling
PARA Method
Paper illustration for Three-Point Estimation.
Decision Making
Three-Point Estimation
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
The PARA Method organizes knowledge into Projects, Areas, Resources, and Archives. It creates order when digital notes, files, and tasks no longer cleanly fit current work.A single estimate for effort, time, or risk often looks too smooth for the real uncertainty behind it. It separates options, evaluation criteria, and open risks. The result is captured as a Three-Point Estimate, Risk Range, and Assumption Notes.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
MediumMediumHigh
Timedifferent
1-3 h Setup, laufend10-30 min je Item30-90 min Setup, danach laufend
Participantsdifferent
11-81-8
Formatdifferent
AsyncWorkshop + asyncWorkshop + async
Outputdifferent
PARA Structure, Project Notes, Knowledge ArchiveThree-Point Estimate, Risk Range, Assumption NotesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
KnowledgeProductivityOrganization
EstimationUncertaintyForecasting
ForecastingFlowDelivery
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Often compared together

Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration for PERT Estimation.
Decision Making
PERT Estimation
Value Stream Mapping workspace showing the question, observations, and next decision.
Delivery
Value Stream Mapping