手法を比較

	管理図を用いた感度分析	感度分析統合実験計画法
分野	実験計画法	実験計画法
系統	Process / pipeline	Process / pipeline
提唱年	Integration practice documented from the 1990s onward	1990s–2000s (formal integration emerged in simulation and engineering optimization literature)
提唱者	Rooted in Shewhart (control charts, 1920s) and Saltelli et al. (global sensitivity analysis, 1990s–2000s); integration practice developed in quality engineering literature	Integrated approach drawing on Saltelli et al. (sensitivity analysis) and Montgomery (DoE); no single originator
種類	Hybrid analytical framework	Hybrid experimental-analytical framework
原典	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975	Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley. ISBN: 9780470870938
別名	SA-SPC integration, control chart sensitivity analysis, SPC sensitivity assessment, sensitivity-enhanced control charting	SA-DoE, SA-integrated DoE, DoE with sensitivity screening, factor screening with sensitivity analysis
関連	6	3
概要	Sensitivity analysis integrated with control charting evaluates how uncertain or varying inputs — such as sample size, subgroup frequency, distribution assumptions, or measurement error — affect the detection performance of a statistical process control chart. By quantifying which parameters most strongly influence chart metrics such as the average run length (ARL) or false alarm rate, engineers can design more robust monitoring schemes and understand where control chart conclusions are fragile.	Sensitivity Analysis-Integrated Design of Experiments (SA-DoE) combines systematic experimental planning with formal sensitivity analysis to identify which input factors most strongly influence a response, then efficiently characterises those factors' effects. By embedding sensitivity screening into the DoE workflow, experimenters avoid wasting trials on inert variables and focus resources on the factors that truly drive system behaviour — making it especially valuable in simulation studies, product engineering, and complex process optimisation.