Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Vícerozměrný regulační diagram× | Vícereakční plánování experimentů× | |
|---|---|---|
| Obor | Plánování experimentů | Plánování experimentů |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions) | 1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s |
| Tvůrce≠ | Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others | Derringer & Suich (desirability function); Montgomery (systematic DoE integration) |
| Typ≠ | Multivariate statistical process monitoring | Experimental optimization methodology |
| Původní zdroj≠ | Hotelling, H. (1947). Multivariate quality control illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of Statistical Analysis (pp. 111–184). McGraw-Hill. link ↗ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Další názvy | multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart | Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE |
| Příbuzné≠ | 6 | 4 |
| Shrnutí≠ | A multi-response control chart simultaneously monitors two or more correlated quality characteristics on a single chart, preserving the correlation structure that univariate charts ignore. Built on Hotelling's T² statistic and its time-weighted extensions (MEWMA, MCUSUM), it detects process shifts that would be missed if each response were charted independently. It is the standard tool in manufacturing and service quality when product performance depends on multiple interrelated outputs. | Multi-response Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets at once. |
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