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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Analisi ad Albero di Eventi Multi-Risposta× | Controllo Statistico di Processo× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1975 (ETA); multi-response extension: 1990s–2000s | 1924–1931 |
| Ideatore≠ | Developed from Event Tree Analysis (originated at WASH-1400 nuclear safety study, U.S. Nuclear Regulatory Commission, 1975); multi-response extension adapted from design-of-experiments and reliability engineering practice | Walter A. Shewhart |
| Tipo≠ | Probabilistic safety and reliability analysis with multiple simultaneous response outcomes | Process monitoring and quality control method |
| Fonte seminale≠ | Stamatelatos, M., Vesely, W., Dugan, J., Fragola, J., Minarick, J., & Railsback, J. (2002). Fault Tree Handbook with Aerospace Applications. NASA Office of Safety and Mission Assurance. link ↗ | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762 |
| Alias | MR-ETA, multi-output event tree analysis, multi-response ETA, probabilistic event tree with multiple responses | SPC, statistical quality control, process control charting, Shewhart control |
| Correlati≠ | 5 | 6 |
| Sintesi≠ | Multi-response Event Tree Analysis (MR-ETA) extends classical event tree analysis by simultaneously tracking multiple system performance or safety response variables across all accident sequences. Instead of evaluating a single outcome (e.g., probability of failure), it propagates several concurrent response metrics — such as damage severity, downtime, cost, and environmental impact — through the event tree branches, enabling richer risk characterization and trade-off decisions under a single probabilistic framework. | Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers. |
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