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Analisis Sensitivitas dengan Bagan Kendali×Desain Eksperimen×
BidangDesain EksperimenDesain Eksperimen
KeluargaProcess / pipelineProcess / pipeline
Tahun asalIntegration practice documented from the 1990s onward1935
PencetusRooted in Shewhart (control charts, 1920s) and Saltelli et al. (global sensitivity analysis, 1990s–2000s); integration practice developed in quality engineering literatureRonald A. Fisher
TipeHybrid analytical frameworkExperimental planning framework
Sumber perintisSaltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗
AliasSA-SPC integration, control chart sensitivity analysis, SPC sensitivity assessment, sensitivity-enhanced control chartingDOE, experimental design, factorial experimentation, planned experimentation
Terkait63
RingkasanSensitivity 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.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.
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ScholarGateBandingkan metode: Sensitivity Analysis with Control Chart · Design of experiments. Diakses 2026-06-18 dari https://scholargate.app/id/compare