Exploring research trends in Lean, Six Sigma and Lean Six Sigma methodologies through a hybrid artificial intelligence approach

Over recent decades, the adoption of operational excellence (OPEX) methods has grown rapidly, boosting productivity and profitability across various industries. This expansion has led to a fragmented body of research, making it difficult to gain a comprehensive understanding through traditional manual or bibliometric methods. To address this challenge, this study integrates machine learning with bibliometric analysis to map the landscape of Lean Six Sigma (LSS) research. Using the largest dataset to date, over 21,000 scientific articles and employing Latent Dirichlet Allocation, we identify 160 distinct topics spanning areas such as patient care, sustainability, supply chain logistics, healthcare operations, and the Toyota Production System. Our approach offers a holistic, unbiased view of the field's evolution, overcoming the limitations of earlier, narrower studies. The findings provide valuable insights for both researchers and practitioners, highlighting emerging trends and guiding future research directions in LSS.