Sammenlign metoder

	Risikobasert Six Sigma DMAIC	Risikobasert statistisk prosesskontroll
Fagfelt	Forsøksdesign	Forsøksdesign
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	1990s–2000s	1920s (SPC foundations); risk-based integration formalized in 2000s–2010s
Opphavsperson	Motorola (Six Sigma, 1986); risk integration formalized in quality engineering literature from the 1990s onward	Integrated from SPC (Shewhart, 1920s; Deming, 1950s) and risk analysis frameworks (FDA ICH Q10, ISO 31000)
Type	Process improvement methodology with embedded risk assessment	Hybrid quality-risk engineering method
Opprinnelig kilde	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.	Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley. ISBN: 978-1119399308
Alias	Risk-integrated DMAIC, DMAIC with risk analysis, Risk-aware Six Sigma, RB-DMAIC	Risk-based SPC, RBSPC, risk-prioritized SPC, risk-informed process monitoring
Relaterte	6	6
Sammendrag	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.	Risk-based statistical process control (Risk-based SPC) is an engineering quality method that integrates formal risk analysis — typically FMEA or a risk matrix — with statistical process monitoring to focus control chart resources on the process parameters that pose the greatest risk to product quality or system safety. Rather than applying control charts uniformly across all variables, risk-based SPC directs tighter monitoring toward high-risk, high-impact process characteristics identified through structured hazard prioritization.