পদ্ধতির তুলনা করুন

	Sensitivity Analysis with Six Sigma DMAIC	পরিসংখ্যানিক প্রক্রিয়া নিয়ন্ত্রণ
ক্ষেত্র	পরীক্ষামূলক নকশা	পরীক্ষামূলক নকশা
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2000s–2010s (applied integration era)	1924–1931
প্রবর্তক	Integration of Six Sigma DMAIC (Motorola / Mikel Harry, 1980s–2000) with sensitivity analysis techniques (Saltelli et al., 1990s–2000s)	Walter A. Shewhart
ধরন	Hybrid process-improvement and uncertainty-quantification pipeline	Process monitoring and quality control method
মৌলিক উৎস	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
অপর নাম	SA-DMAIC, DMAIC sensitivity analysis, sensitivity-informed Six Sigma, Six Sigma sensitivity integration	SPC, statistical quality control, process control charting, Shewhart control
সম্পর্কিত	5	6
সারসংক্ষেপ	Sensitivity analysis integrated with Six Sigma DMAIC augments the classic Define-Measure-Analyze-Improve-Control cycle with formal quantification of how much each input variable contributes to output variation. By embedding local or global sensitivity indices inside the Analyze phase, practitioners move beyond correlation screening to rigorously rank which process factors drive defect rates, guiding improvement resources to where they matter most.	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.