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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse des Modes de Défaillance et de leurs Effets× | Semi-Quantitative Risk Matrix Analysis× | |
|---|---|---|
| Domaine≠ | Prise de décision | Disaster Studies |
| Famille≠ | MCDM | Process / pipeline |
| Année d'origine≠ | 1995 | 2019 |
| Auteur d'origine≠ | Stamatis, D. H. | ISO/IEC 31010 (standardized practice); critical analysis by L. A. Cox |
| Type≠ | Risk priority via product of O·S·D ratings | Semi-quantitative consequence-likelihood rating and ranking pipeline |
| Source fondatrice≠ | Stamatis, D. H. (1995). Failure Mode and Effect Analysis: FMEA from Theory to Execution. ASQ Quality Press ISBN: 978-0-87389-300-8 | International Organization for Standardization. (2019). IEC 31010:2019 Risk management — Risk assessment techniques. ISO/IEC, Geneva. link ↗ |
| Alias≠ | — | Risk Matrix Analysis, Consequence-Likelihood Matrix, Probability-Impact Matrix, Risk Rating Matrix |
| Apparentées≠ | 8 | 3 |
| Résumé≠ | FMEA (Failure Mode and Effects Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Stamatis, D. H. in 1995. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. | Semi-quantitative risk matrix analysis rates each risk on ordinal likelihood and consequence scales and combines the two in a grid to assign a risk level that drives prioritization. It is the workhorse of practical risk management: ISO/IEC 31010 lists the consequence-likelihood matrix among its standard techniques precisely because it lets analysts compare many disparate risks quickly without the data demands of a full quantitative model. The 'semi-quantitative' label captures its hybrid character — ordinal categories such as 'rare' or 'catastrophic' are anchored to rough numeric bands, giving more discipline than a purely verbal judgment but far less than a probabilistic calculation. The method's popularity is matched by sharp critique: L. A. Cox's 2008 analysis in Risk Analysis showed that poorly designed matrices can rank risks incorrectly, compress very different risks into the same cell, and even perform worse than random, making careful scale design and consistency checks essential rather than optional. |
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