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| Sensitivitätsanalyse mit FMEA× | Statistische Prozesslenkung (SPC)× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1990s–2000s (systematic integration) | 1924–1931 |
| Urheber≠ | Grumman Aircraft (FMEA origin, 1950s); sensitivity analysis integration developed by reliability engineering community | Walter A. Shewhart |
| Typ≠ | Hybrid risk analysis and sensitivity technique | Process monitoring and quality control method |
| Wegweisende Quelle≠ | Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press. ISBN: 978-0873895989 | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762 |
| Aliasnamen | SA-FMEA, FMEA with sensitivity analysis, sensitivity-enhanced FMEA, SA-integrated FMEA | SPC, statistical quality control, process control charting, Shewhart control |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Sensitivity analysis with failure mode and effects analysis (SA-FMEA) combines classical FMEA risk scoring with systematic sensitivity analysis to determine which input parameters — severity, occurrence, and detectability ratings — drive the Risk Priority Number (RPN) most strongly. This integration helps teams focus improvement resources where they matter most, revealing how uncertain or variable scoring assumptions propagate into final risk rankings. | 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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