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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Analisi delle Cause Radice Multi-Risposta× | Progettazione di Esperimenti Multi-Risposta× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1990s–2000s (multi-response extension of classical RCA) | 1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s |
| Ideatore≠ | Root Cause Analysis tradition (Kepner-Tregoe, Ishikawa, Deming); multi-response extension in Six Sigma and quality engineering practice | Derringer & Suich (desirability function); Montgomery (systematic DoE integration) |
| Tipo≠ | Systematic problem-solving method | Experimental optimization methodology |
| Fonte seminale≠ | Andersen, B., & Fagerhaug, T. (2006). Root Cause Analysis: Simplified Tools and Techniques (2nd ed.). ASQ Quality Press. ISBN: 978-0873896924 | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Alias | Multi-KPI RCA, Multi-output RCA, Multi-response RCA, MRCA | Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE |
| Correlati≠ | 6 | 4 |
| Sintesi≠ | Multi-response Root Cause Analysis (MRCA) is a structured problem-solving method that identifies the underlying causes of failures or deviations across multiple simultaneous response variables (KPIs, quality characteristics, or process outputs). It extends classical RCA to settings where a single root cause can propagate into several observed defects or performance degradations at once, which is common in manufacturing, engineering, and service-quality contexts. | 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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