方法对比

	Simulation-Assisted Statistical Process Control	实验设计
领域	实验设计	实验设计
方法族	Process / pipeline	Process / pipeline
起源年份	1980s–present	1935
提出者	Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward	Ronald A. Fisher
类型	Hybrid quantitative method	Experimental planning framework
开创性文献	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.
别名	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC	DOE, experimental design, factorial experimentation, planned experimentation
相关	6	3
摘要	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.