Confronta i metodi

	DMAIC Six Sigma basato sul rischio	Controllo Statistico di Processo
Campo	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1990s–2000s	1924–1931
Ideatore	Motorola (Six Sigma, 1986); risk integration formalized in quality engineering literature from the 1990s onward	Walter A. Shewhart
Tipo	Process improvement methodology with embedded risk assessment	Process monitoring and quality control method
Fonte seminale	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
Alias	Risk-integrated DMAIC, DMAIC with risk analysis, Risk-aware Six Sigma, RB-DMAIC	SPC, statistical quality control, process control charting, Shewhart control
Correlati	6	6
Sintesi	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.