(untitled) (Contemporary Journal of Social Science Review, 2024)
The focus of this research is to explore how AI can enhance the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) model to transform the existing continuous process improvement and develop a greater economic impact for various industries. AI’s strengths of data analytics and, in particular, machine learning enable business to detect problematic areas in manufacturing processes with high accuracy in real time, thus preventing such issues from exacerbating into crisis. It also saves on resources which have to be spent in case of manual intervention while at the same ensuring maximum operational efficiency. The combination of the predictive analysis of AI with the rigor of Six Sigma promotes productivity in diverse organizations, implementing strict standards of quality to support constant improvement in performance. This integration in particularly applies in supply chain management where the operational efficiency is of critical importance in terms of sustainability and results. This way AI automation and predictive analytics saves resources, and overall organization waste is reduced drastically. Notably, this alignment is mostly aligned with economic development and environmental standards of sustainability that create a roadmap for establishing operational best practices and sustainability. Lastly, this innovative approach proves that integration of AI and Six Sigma can revolutionize business parental prey to model a world where efficiency is entwined with environmental sustainability as drivers of business competition.