What is it about?
Organizations have adopted Six Sigma (SS) as a successful process improvement initiative. However, the advancement of digitalization and the consequent increase in data complexity have demanded more sophisticated analyses, highlighting the limitations of traditional SS tools in dealing with this scenario. Studies suggest integrating new knowledge, such as Data Science (DS) techniques, to increase the analytical capabilities of SS. This work aims to develop a framework that integrates DS techniques with the SS methodology, enhancing its effectiveness and expanding its capabilities in complex, data-rich environments.
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Why is it important?
This study is a pioneering effort to evaluate DS techniques in SS through a global survey. It presents an innovative, comprehensive, and detailed framework that aids project execution and selecting the most suitable techniques. It is a practical guide for implementing, streamlining decision-making, and enhancing project efficiency.
Perspectives
We surveyed 348 SS experts from 49 countries to identify the most promising DS tools for SS. Following this survey, the Design Science Research Methodology (DSRM) was combined with the Delphi method to develop the proposed framework. This approach allowed us to leverage DSRM's structured process and the insights from a panel of experts to create and refine the framework collaboratively and systematically.
Ana Oliveira
Universidade do Minho
Read the Original
This page is a summary of: Framework for integrating data science and six sigma: results of a global survey and panel of specialists, International Journal of Lean Six Sigma, December 2025, Emerald,
DOI: 10.1108/ijlss-04-2025-0085.
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