• Mustapha Radi

This particular research study explores the emerging synergy among the methodologies of Six Sigma and the Artificial Intelligence particularly in the manufacturing sector. Rooted deeply in the different sources of secondary data, the study particularly outlines the historical role of Six Sigma in restructuring the wide range of manufacturing processes and highlighting the transformative capacities that are introduced by the AI in predictive analytics, real time optimization of process and adaptability of the process. By exploring various case studies from industry giants like General Electric and Toyota, the integration's real-world implications and the ensuing efficiency gains are underscored, with some firms witnessing up to a 25% increase in operational efficiency within the first year of integration. Yet, the journey is not devoid of challenges, encompassing both cultural and technical hurdles, especially in terms of data management and the inherent resistance to transformative changes within longstanding organizational paradigms. Anticipated future trends, such as self-healing manufacturing processes and augmented reality training modules, indicate an intensifying fusion of these two domains. Despite its insights, the research recognizes its limitations, primarily its reliance on secondary sources and the challenge of capturing the rapid evolution in AI technologies. The study concludes by underlining the practical implications, recommending areas for future research, and acknowledging the potential for further technological advancements in the field.

"Integrating Artificial Intelligence-Powered Process Optimization with Six Sigma for Enhanced Manufacturing Efficiency", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.a265-a279, November-2023, Available :http://www.jetir.org/papers/JETIR2311039.pdf

"Integrating Artificial Intelligence-Powered Process Optimization with Six Sigma for Enhanced Manufacturing Efficiency", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppa265-a279, November-2023, Available at : http://www.jetir.org/papers/JETIR2311039.pdf