Framework for integrating data science and six sigma: results of a global survey and panel of specialists

Purpose Although Six Sigma (SS) is effective in process improvement, digitalization and data complexity have exposed limitations in its traditional tools. To address this challenge, this study aims to develop a framework that integrates data science (DS) techniques, enhancing the analytical capacity and applicability of SS in complex contexts. Design/methodology/approach The authors conducted a global survey involving experts from 49 countries, combining design science research methodology (DSRM) with the Delphi method. This approach provided a solid empirical foundation while maintaining scientific rigor and practical validation of the proposed framework. Findings The authors propose an innovative flowchart-based framework that integrates DS into the SS methodology. By structuring decision-making based on the context and complexity of the problem, the framework supports the selection of analytical tools, strengthening methodological rigor, analytical capacity and the SS applicability. Originality/value To the best of the authors’ knowledge, this study is pioneering in integrating DS into SS through a flowchart-based framework, developed from a global survey and combining DSRM with the Delphi method. Unlike previous models, it does not merely list tools but guides the reasoning process step by step, allowing the selection of the most appropriate technique according to the complexity and nature of the problem, making it a practical resource that enhances project efficiency.