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| Симулационно подпомогнато разгръщане на функциите на качеството× | Планиране на експерименти× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1990s–2000s (QFD: 1966; simulation integration: ~1995–2005) | 1935 |
| Създател≠ | Yoji Akao (QFD foundation); simulation integration developed by engineering researchers in 1990s–2000s | Ronald A. Fisher |
| Тип≠ | Hybrid engineering design and quality planning method | Experimental planning framework |
| Основополагащ източник≠ | Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press. ISBN: 978-0915299416 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Други названия | SA-QFD, simulation-integrated QFD, simulation-driven house of quality, QFD with simulation | DOE, experimental design, factorial experimentation, planned experimentation |
| Свързани≠ | 6 | 3 |
| Резюме≠ | Simulation-assisted quality function deployment (SA-QFD) integrates computational simulation into the classic QFD framework to replace or supplement costly physical prototypes when evaluating how engineering design decisions satisfy customer requirements. By embedding simulation models — such as finite element analysis, discrete-event simulation, or system dynamics — within the House of Quality matrix, engineers can rapidly quantify the impact of technical characteristics on customer satisfaction and iteratively refine design priorities before committing to production. | Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences. |
| ScholarGateНабор от данни ↗ |
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