Causes of Non-normality of Monitored Quality Characteristics in Process Capability Analysis

Monika Sekaninová

Quality Innovation Prosperity2025https://doi.org/10.12776/qip.v29i3.2266article
AJG 1
Weight
0.50

What the paper says

In process capability analysis, violation of the normality assumption is considered a non-standard situation. This paper defines and categorises causes of non-normality in manufacturing processes, which are essential for quality planning, control, and selecting appropriate analytical procedures. Based on a literature review and systematic investigation, four main categories have been identified. Each category is analysed in detail with practical implications for capability analysis. The paper provides a comprehensive framework for identifying non-normality causes and guiding further analysis steps. Methodology/Approach: Systematic literature review and expert synthesis of causes, supported by practical examples and categorisation into four key areas Findings: four categories of non-normality causes identified; each requires a specific analytical approach before capability analysis can proceed Research Limitation/Implication: Focuses on categorisation; future work should develop automated detection methods and validate solutions across diverse industries Originality/Value of paper: Provides the first comprehensive framework for identifying non-normality causes in manufacturing, bridging theory and industrial practice

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https://doi.org/https://doi.org/10.12776/qip.v29i3.2266

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@article{monika2025,
  title        = {{Causes of Non-normality of Monitored Quality Characteristics in Process Capability Analysis}},
  author       = {Monika Sekaninová},
  journal      = {Quality Innovation Prosperity},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.12776/qip.v29i3.2266},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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