Methoden vergleichen

	Risikobasierter Six Sigma DMAIC	Statistische Prozesslenkung (SPC)
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990s–2000s	1924–1931
Urheber	Motorola (Six Sigma, 1986); risk integration formalized in quality engineering literature from the 1990s onward	Walter A. Shewhart
Typ	Process improvement methodology with embedded risk assessment	Process monitoring and quality control method
Wegweisende Quelle	De Mast, J., & Lokkerbol, J. (2012). An analysis of the Six Sigma DMAIC method from the perspective of problem solving. International Journal of Production Economics, 139(2), 604–614.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Aliasnamen	Risk-integrated DMAIC, DMAIC with risk analysis, Risk-aware Six Sigma, RB-DMAIC	SPC, statistical quality control, process control charting, Shewhart control
Verwandt	6	6
Zusammenfassung	Risk-based Six Sigma DMAIC embeds structured risk assessment — typically failure mode and effects analysis (FMEA), risk priority numbers (RPN), or probabilistic risk tools — at each stage of the standard DMAIC cycle. The goal is not only to reduce defects and variation but to prioritize improvement actions by their risk consequence, ensuring that critical failure modes are addressed before less impactful ones. It is widely applied in manufacturing, healthcare, aerospace, and process industries where both quality and safety are at stake.	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.