Compara mètodes

	Anàlisi de sensibilitat amb gràfic de control	Control Estadístic de Processos
Camp	Disseny experimental	Disseny experimental
Família	Process / pipeline	Process / pipeline
Any d'origen	Integration practice documented from the 1990s onward	1924–1931
Autor original	Rooted in Shewhart (control charts, 1920s) and Saltelli et al. (global sensitivity analysis, 1990s–2000s); integration practice developed in quality engineering literature	Walter A. Shewhart
Tipus	Hybrid analytical framework	Process monitoring and quality control method
Font seminal	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	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Àlies	SA-SPC integration, control chart sensitivity analysis, SPC sensitivity assessment, sensitivity-enhanced control charting	SPC, statistical quality control, process control charting, Shewhart control
Relacionats	6	6
Resum	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.	Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers.