Comparar métodos

	Control Estadístico de Procesos Asistido por Simulación	Diseño de Experimentos
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1980s–present	1935
Autor original	Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward	Ronald A. Fisher
Tipo	Hybrid quantitative method	Experimental planning framework
Fuente seminal	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0470169926	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC	DOE, experimental design, factorial experimentation, planned experimentation
Relacionados	6	3
Resumen	Simulation-assisted statistical process control (SA-SPC) combines computer simulation — typically Monte Carlo or discrete-event simulation — with classical SPC methods to design, test, and calibrate control charts and monitoring schemes before or alongside deployment on a real production process. Rather than relying solely on closed-form analytical assumptions, SA-SPC uses simulated data to evaluate chart performance under realistic, often non-normal process conditions.	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.