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Anàlisi de Causes Fonamentals Multiresposta×Disseny d'experiments multiresposta×
CampDisseny experimentalDisseny experimental
FamíliaProcess / pipelineProcess / pipeline
Any d'origen1990s–2000s (multi-response extension of classical RCA)1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s
Autor originalRoot Cause Analysis tradition (Kepner-Tregoe, Ishikawa, Deming); multi-response extension in Six Sigma and quality engineering practiceDerringer & Suich (desirability function); Montgomery (systematic DoE integration)
TipusSystematic problem-solving methodExperimental optimization methodology
Font seminalAndersen, B., & Fagerhaug, T. (2006). Root Cause Analysis: Simplified Tools and Techniques (2nd ed.). ASQ Quality Press. ISBN: 978-0873896924Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗
ÀliesMulti-KPI RCA, Multi-output RCA, Multi-response RCA, MRCAMulti-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE
Relacionats64
ResumMulti-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.Multi-response Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets at once.
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ScholarGateCompara mètodes: Multi-response Root Cause Analysis · Multi-response Design of Experiments. Recuperat el 2026-06-18 de https://scholargate.app/ca/compare