🤖 AI Summary
This study addresses the challenge of accurately assessing univariate process capability indices (PCIs) in manufacturing under atypical conditions where standard assumptions—particularly normality—are often violated. To overcome this limitation, the authors propose a systematic and unified PCI analysis workflow that integrates outlier detection, normality assessment, optimal distribution fitting, and corresponding PCI computation tailored to diverse distributional assumptions and data characteristics. By offering a structured and actionable framework, the approach enables practitioners to select the most appropriate PCI based on actual process behavior, thereby significantly enhancing both the accuracy and applicability of capability evaluations. This methodology provides a practical and streamlined guide for quality control and process improvement in real-world industrial settings where data frequently deviate from idealized statistical conditions.
📝 Abstract
This paper presents a comprehensive review of univariate process capability indices (PCIs), which are critical metrics for assessing how effectively a manufacturing process satisfies customer specifications based on a single quality characteristic. The primary objective of this review is to develop practical procedural workflows for conducting process capability analysis under various preconditions, including those less frequently addressed scenarios in existing literature. Key analytical components, such as outlier detection, normality test, and best distribution fitting, are integrated into the proposed framework to ensure accurate and robust capability assessments. By systematically evaluating a range of methodologies, this study offers guidance for researchers and practitioners in selecting the most appropriate PCIs for specific process conditions. Ultimately, the work aims to simplify the complexity of PCI analysis while enhancing its precision and utility in quality control and process improvement efforts.