Comparar métodos

	Optimización Asistida por Seis Sigma DMAIC	Diseño de Experimentos
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1990s–2000s (integration period)	1935
Autor original	Six Sigma: Motorola (Bill Smith, Mikel Harry, 1986); optimization integration formalized in engineering literature through the 1990s–2000s	Ronald A. Fisher
Tipo	Process improvement framework with embedded optimization	Experimental planning framework
Fuente seminal	Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring Business Excellence, 6(4), 20-27.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	Optimization-integrated DMAIC, DMAIC with optimization, Six Sigma optimization framework, Opt-DMAIC	DOE, experimental design, factorial experimentation, planned experimentation
Relacionados	5	3
Resumen	Optimization-assisted Six Sigma DMAIC embeds formal mathematical optimization — response surface methods, metaheuristics, or multi-objective solvers — into the Improve phase of the DMAIC cycle. Rather than relying solely on engineering judgment or one-factor-at-a-time trials, the approach uses designed experiments to build a predictive model of the process and then applies an optimization algorithm to locate factor settings that best satisfy quality, cost, or multiple competing performance targets simultaneously.	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.