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| Criterion |  Operations PDCA Cycle |  Operations Causal Factor Analysis |  Innovation Design Sprint |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | For an improvement that still has to prove itself in everyday work, the method runs it through small learning loops. It connects planning, checking, and standardization into a repeatable learning mode. | For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events. | When the product question is unclear and time pressure is high, the method takes a team from an open idea to a testable solution. It connects problem understanding, decision, and learning in one compact format, lowering the risk of spending a lot of energy on a mere guess. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. |
Complexitydifferent | Low | High | High | High |
Timedifferent | 1 h bis mehrere Wochen | 2-6 h | 4-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-8 | 3-10 | 5-8 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Prototype, Test Findings, Decision Rationale | Experiment results, Decision log, Learning summary |
Tagsno overlap | Continuous improvementLeanExperiments | CausalityIncidentRoot causeTimeline | Design sprintPrototypeValidationInnovation | ExperimentsGrowthAnalyticsValidation |