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| Simulationsgestützte Prozessfähigkeitsanalyse× | Monte-Carlo-Simulation× | |
|---|---|---|
| Fachgebiet≠ | Versuchsplanung | Entscheidungsfindung |
| Familie≠ | Process / pipeline | MCDM |
| Entstehungsjahr≠ | 1980s–1990s (mature practice by mid-1990s) | 1949 |
| Urheber≠ | Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues) | Metropolis, N., Ulam, S. |
| Typ≠ | Quantitative engineering quality method | Robustness wrapper — Monte Carlo uncertainty propagation |
| Wegweisende Quelle≠ | Kotz, S., & Lovelace, C. R. (1998). Process Capability Indices in Theory and Practice. Arnold. ISBN: 978-0340691281 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliasnamen≠ | Monte Carlo process capability, simulation-based Cpk analysis, stochastic capability analysis, virtual process capability study | — |
| Verwandt≠ | 6 | 0 |
| Zusammenfassung≠ | Simulation-assisted process capability analysis combines Monte Carlo simulation with classical capability indices (Cp, Cpk, Cpm) to evaluate whether a process can consistently meet specification limits when direct measurement is costly, dangerous, or impractical. By propagating input distributions through a process model, the analyst obtains a simulated output distribution and derives capability metrics without waiting for physical production runs. The approach is especially valuable during product design, process scale-up, and tolerance stack-up studies. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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