Σύγκριση μεθόδων

	Ανάλυση Αιτιών Πολλαπλών Αποκρίσεων (Multi-response Root Cause Analysis)	Στατιστικός Έλεγχος Διεργασιών
Πεδίο	Πειραματικός Σχεδιασμός	Πειραματικός Σχεδιασμός
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1990s–2000s (multi-response extension of classical RCA)	1924–1931
Δημιουργός	Root Cause Analysis tradition (Kepner-Tregoe, Ishikawa, Deming); multi-response extension in Six Sigma and quality engineering practice	Walter A. Shewhart
Τύπος	Systematic problem-solving method	Process monitoring and quality control method
Θεμελιώδης πηγή	Andersen, B., & Fagerhaug, T. (2006). Root Cause Analysis: Simplified Tools and Techniques (2nd ed.). ASQ Quality Press. ISBN: 978-0873896924	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Εναλλακτικές ονομασίες	Multi-KPI RCA, Multi-output RCA, Multi-response RCA, MRCA	SPC, statistical quality control, process control charting, Shewhart control
Συναφείς	6	6
Σύνοψη	Multi-response Root Cause Analysis (MRCA) is a structured problem-solving method that identifies the underlying causes of failures or deviations across multiple simultaneous response variables (KPIs, quality characteristics, or process outputs). It extends classical RCA to settings where a single root cause can propagate into several observed defects or performance degradations at once, which is common in manufacturing, engineering, and service-quality contexts.	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.