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| 민감도 분석과 관리도 병행× | 공정 능력 분석을 통한 민감도 분석× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Integration practice documented from the 1990s onward | 1986–2000s (Cp/Cpk indices from Kane 1986; integration formalized in Six Sigma era) |
| 창시자≠ | Rooted in Shewhart (control charts, 1920s) and Saltelli et al. (global sensitivity analysis, 1990s–2000s); integration practice developed in quality engineering literature | Synthesized from work by V. E. Kane (process capability indices) and A. Saltelli (sensitivity analysis); integrated in Six Sigma and quality engineering practice |
| 유형≠ | Hybrid analytical framework | Quantitative engineering analysis |
| 원전≠ | 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 | Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0470169926 |
| 별칭 | SA-SPC integration, control chart sensitivity analysis, SPC sensitivity assessment, sensitivity-enhanced control charting | Sensitivity-Capability Analysis, PCA with Sensitivity Analysis, Process Capability Sensitivity Study, Cp/Cpk Sensitivity Analysis |
| 관련≠ | 6 | 5 |
| 요약≠ | 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 with process capability analysis is a quantitative engineering method that combines the measurement of process performance — via capability indices such as Cp and Cpk — with systematic variation of input factors to identify which factors most strongly influence whether a process meets its specification limits. It is widely used in Six Sigma projects, manufacturing quality improvement, and Design of Experiments contexts to prioritize where corrective action will yield the greatest gain in process capability. |
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