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Process / pipelineEngineering methods

Sensitivity Analysis-Integrated Response Surface Methodology

Sensitivity analysis-integrated RSM couples a structured experimental design with a formal sensitivity analysis of the fitted response surface model. After estimating a polynomial surrogate from designed experiments, global or local sensitivity indices are computed to quantify each input factor's relative contribution to output variability. This allows practitioners to identify which factors truly drive the response before committing to full optimization, reducing cost and improving the reliability of the final optimum.

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Sources

  1. Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916018
  2. 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

Related methods

Box-Behnken DesignCentral Composite DesignDesign of experimentsOptimization-assisted response surface methodologyResponse Surface Methodology

Referenced by

Sensitivity analysis with failure mode and effects analysisSensitivity Analysis with Reliability AnalysisSensitivity analysis-integrated full factorial design

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ScholarGate — Sensitivity analysis-integrated response surface methodology (Sensitivity Analysis-Integrated Response Surface Methodology). Retrieved 2026-06-04 from https://scholargate.app/en/experimental-design/sensitivity-analysis-integrated-response-surface-methodology