Confronta i metodi

	Six Sigma DMAIC Ibrido	Design of Experiments	Controllo Statistico di Processo
Campo	Disegno sperimentale	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Process / pipeline	Process / pipeline
Anno di origine	1980s (Six Sigma); Hybrid/Lean integration widely adopted ~2000–2002	1935	1924–1931
Ideatore	Hybrid formalized through Lean Six Sigma integration; foundational DMAIC rooted in Motorola's Six Sigma program (Bill Smith, Mikel Harry)	Ronald A. Fisher	Walter A. Shewhart
Tipo	Process improvement and quality management framework	Experimental planning framework	Process monitoring and quality control method
Fonte seminale	George, M. L. (2002). Lean Six Sigma: Combining Six Sigma Quality with Lean Speed. McGraw-Hill. ISBN: 978-0071385213	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Alias	Lean Six Sigma DMAIC, Hybrid DMAIC, Integrated Six Sigma DMAIC, DMAIC Hybrid Framework	DOE, experimental design, factorial experimentation, planned experimentation	SPC, statistical quality control, process control charting, Shewhart control
Correlati	2	3	6
Sintesi	Hybrid Six Sigma DMAIC combines the rigorous five-phase DMAIC cycle (Define, Measure, Analyze, Improve, Control) with complementary methodologies — most commonly Lean principles, Agile practices, or Design Thinking — to address quality defects and process inefficiencies simultaneously. By integrating speed-focused tools from Lean with the statistical discipline of Six Sigma, hybrid approaches close the gap that pure Six Sigma frameworks sometimes leave when waste elimination and cycle-time reduction are equally critical goals.	Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.	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.