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| Criterion |  Product Discovery Kano Model |  Operations Waste Analysis |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
Purposedifferent | When features need to be distinguished by their effect on satisfaction, it makes expectations and surprises comparable. It separates basic needs, performance contribution, and delight. | For a process that keeps people busy but creates little value, the method exposes waste. It directs attention to unnecessary movement, waiting times, rework, and overdelivery. | 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 | Medium | Low | High |
Timedifferent | 1-2 Tage | 45-120 min | 30-90 min Setup, danach laufend |
Participantsdifferent | 10-50 | 2-8 | 1-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Kano Matrix, Feature Classes, Priority Themes | Waste Map, Prioritized Waste, Improvement Backlog | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | SatisfactionPrioritizationCustomer | WasteLeanProcess improvement | ForecastingFlowDelivery |
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