<![CDATA[Aims - This study aims to present a novel and comprehensive mapping of the integration between Lean Six Sigma (LSS) and Business Analytics (BA) through a systematic review of publications from 2020 to 2025, and to develop a conceptual LSS–BA framework based on four analytical layers. Methodology - This study employed a PRISMA-guided Systematic Literature Review with a two-stage approach: 20 Q1–Q2 journal articles were analyzed to map general Lean Six Sigma trends, while 10 selected articles were examined in depth to identify Business Analytics integration and data-driven practices. Findings - The review reveals that LSS implementation remains highly dependent on traditional DMAIC tools and methods, while the use of advanced analytical approaches such as machine learning, predictive analytics, and text mining is still limited, particularly in the Analyze and Control phases. Digital integration appears only in a small proportion of the reviewed studies. Research Limitations - The main limitations include the lack of detailed quantitative evidence in existing publications and the uneven adoption of BA practices across LSS studies. These findings imply the need for stronger empirical validation and broader digital transformation in continuous improvement initiatives. Originality - This study offers original value by providing a structured synthesis of recent LSS–BA research and proposing a four-layer conceptual framework that can guide future development of data-driven and prescriptive process improvement systems.]]>
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