Methoden vergleichen

	Robuste Fehlermöglichkeits- und Einflussanalyse (Robust FMEA)	Statistische Prozesslenkung (SPC)
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1980s–1990s	1924–1931
Urheber	Extension of traditional FMEA (MIL-P-1629, 1949) integrated with Taguchi robust design philosophy (Genichi Taguchi, 1980s)	Walter A. Shewhart
Typ	Risk analysis with variability quantification	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	Robust FMEA, Noise-Aware FMEA, Variability-Integrated FMEA, Robustness-Based FMEA	SPC, statistical quality control, process control charting, Shewhart control
Verwandt	4	6
Zusammenfassung	Robust Failure Mode and Effects Analysis extends the classical FMEA framework by explicitly incorporating noise factors, parameter variability, and environmental variation into the risk assessment process. Rather than treating failure likelihood as a single deterministic estimate, it uses robust design principles — most notably from Taguchi's quality engineering — to evaluate how process variability and uncontrollable noise factors influence the probability and severity of each failure mode, yielding risk priority numbers that reflect real-world variability.	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.