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| Analiza zdolności procesu wspomagana symulacją× | Analiza zdolności procesu odpornej na wartości odstające× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1980s–1990s (mature practice by mid-1990s) | 1990s–2000s |
| Twórca≠ | Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues) | Extended from classical PCA (Kane, 1986; Juran, 1974) via robust statistics (Huber, 1981); formalized for capability indices by Tong & Chen (1998) and Pearn & Kotz (1994) |
| Typ≠ | Quantitative engineering quality method | Quantitative quality engineering method |
| Źródło pierwotne≠ | Kotz, S., & Lovelace, C. R. (1998). Process Capability Indices in Theory and Practice. Arnold. ISBN: 978-0340691281 | Maravelakis, P. E., Bersimis, S., Panaretos, J., & Psarakis, S. (2004). Identifying the out of control variable in a multivariate control chart. Communications in Statistics - Theory and Methods, 33(10), 2499–2510. link ↗ |
| Inne nazwy | Monte Carlo process capability, simulation-based Cpk analysis, stochastic capability analysis, virtual process capability study | Robust PCA, Robust Capability Indices, Outlier-Resistant Capability Analysis, Robust Cpk Analysis |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | 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. | Robust process capability analysis extends classical capability indices (Cp, Cpk, Ppk) by replacing the sample mean and standard deviation with robust location and scale estimators — such as the median, trimmed mean, MAD, or IQR-based spread — so that outliers and non-normal process distributions do not inflate or distort the capability estimate. The result is a more reliable assessment of whether a manufacturing or service process can consistently meet specification limits. |
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