Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiza hibridă a capabilității proceselor× | Analiza capacității proceselor multi-răspuns× | |
|---|---|---|
| Domeniu | Design experimental | Design experimental |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1990s–2000s | 1993–1994 (foundational multivariate indices) |
| Autorul original≠ | Various; systematised through extensions of Kane (1986) and Pearn, Kotz & Johnson (1992) | Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm) |
| Tip≠ | Quantitative process quality assessment | Quantitative quality / process assessment method |
| Sursa seminală≠ | Pearn, W. L., Kotz, S., & Johnson, N. L. (1992). Distributional and inferential properties of process capability indices. Journal of Quality Technology, 24(4), 216–231. DOI ↗ | Taam, W., Subbaiah, P., & Liddy, J. W. (1993). A note on multivariate capability indices. Journal of Applied Statistics, 20(3), 339–351. link ↗ |
| Denumiri alternative | hybrid PCA, integrated process capability analysis, combined capability index analysis, multi-method process capability assessment | MRPCA, multivariate process capability, multi-characteristic capability analysis, vector process capability |
| Înrudite | 6 | 6 |
| Rezumat≠ | Hybrid process capability analysis combines two or more capability assessment techniques — for example, classical indices (Cp, Cpk) with fuzzy logic, bootstrap inference, or Bayesian estimation — to overcome the limitations of any single approach. By integrating complementary methods, it delivers more robust capability statements for non-normal, asymmetric, or short-run processes where standard indices alone would mislead quality decisions. | Multi-response process capability analysis extends classical single-response capability indices (Cp, Cpk) to situations where a process must simultaneously satisfy specification limits on two or more correlated quality characteristics. Rather than evaluating each response in isolation, it assesses the joint probability that all characteristics fall within their respective tolerance regions, yielding a more realistic picture of overall process performance in multi-characteristic manufacturing and engineering settings. |
| ScholarGateSet de date ↗ |
|
|