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Redefining DMAIC With AI and Real-Time Data
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Author(s): G. Boopathy (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India), G. Chandra Bose (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India), M. Gayathri (Amrita Vishwa Vidyapeetham, Chennai, India)and Mohit Hemanth Kumar (Alliance University, India)
Copyright: 2026
Pages: 28
Source title:
Transformative Lean Six Sigma Techniques for the Quality 5.0 Paradigm
Source Author(s)/Editor(s): José Carlos Vieira de Sá (Instituto Superior de Engenharia do Porto, Instituto Politécnico do Porto, Portugal), Shubham Sharma (Shoolini University, India), Sunil Kumar (Shoolini University, India)and Manjunath B.R. (Tecnologico de Monterrey, Mexico)
DOI: 10.4018/979-8-3373-0943-9.ch005
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Abstract
The combination of Artificial Intelligence (AI) and real time data analytics with the typical Lean Six Sigma (LSS) DMAIC framework is investigated in this chapter as it includes the use of Industry 5.0 principles. As an alternative to more static data analysis, it offers more dynamic, predictive, and prescriptive decision-making procedures that allow these organizations to better optimize their operations. Machine learning and Internet of Things (IoT) are also exciting AI skills by which some problems can be proactively identified to augment operational efficiency and quality in alignment with human-centric, sustainable and resilient manufacturing paradigms. It also presents a new conceptual framework that adds to every phase of the DMAIC cycle and consequently turns this reactive system into a predictive system, supporting the continuous improvement and tackling sustainability, efficiency, and the collaboration challenges. This integration helps firms attain operational excellence in Industry 5.0 by bridge the gaps between new, technology-based methods and conventional processes.
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