A Systematic Literature Review: Implementation of Lean, Six Sigma and Machine Learning in Manufacturing Industry

The global manufacturing industry is growing rapidly, requiring industries to create strategic innovations to improve quality and efficiency. This paper presents a comprehensive systematic literature review on integrating Lean Six Sigma (LSS) and Machine Learning (ML) within the manufacturing sector. By synthesizing key studies, the review uncovers how the fusion of LSS's process improvement methodologies with ML's advanced predictive analytics and real-time decision-making capabilities can propel manufacturing operations to new heights of performance and sustainability. The findings underscore the transformative potential of integrating ML into LSS frameworks, enabling proactive process optimization, enhanced defect prevention, and dynamic operational adjustments. This paper contributes to the academic and practical knowledge base by highlighting current practices and pinpointing significant gaps in the literature. It calls for future research to explore industry-specific applications, develop tailored implementation frameworks, and empirically validate the integration’s impact across diverse manufacturing environments. Moreover, the review offers actionable insights for manufacturing managers, providing a roadmap for the successful adoption and scaling of LSS and ML integration. The conclusions drawn from this study aim to guide future research efforts and foster the development of more adaptive, data-driven, and resilient manufacturing processes.