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Research Paper | Industrial Engineering | Volume 15 Issue 7, July 2026 | Pages: 1896 - 1899 | United States
Predictive Quality Control: Merging Machine Learning with Lean Six Sigma
Abstract: This paper proposes a conceptual framework that integrates Lean Six Sigma with machine learning to enable predictive quality control in modern manufacturing systems. The framework embeds machine learning models within the DMAIC methodology to support early defect prediction, improved process capability, and proactive decision making. Random Forest and Support Vector Machine based approaches are discussed as representative predictive techniques for enhancing quality management (Breiman, 2001; Cortes & Vapnik, 1995). The paper also examines the theoretical foundations of implementation challenges related to data quality, organizational readiness, and model governance while highlighting implications for engineering managers leading Industry 4.0 initiatives (Sordan et al., 2024). The proposed framework provides a structured foundation for future empirical validation and practical adoption in smart manufacturing environments.
Keywords: Lean Six Sigma, Machine Learning, Predictive Quality Control, DMAIC, Process Capability, Predictive Analytics, Smart Manufacturing, Industry 4.0
How to Cite?: Harsimran Kaur, "Predictive Quality Control: Merging Machine Learning with Lean Six Sigma", Volume 15 Issue 7, July 2026, International Journal of Science and Research (IJSR), Pages: 1896-1899, https://www.ijsr.net/getabstract.php?paperid=SR26721024521, DOI: https://dx.doi.org/10.21275/SR26721024521