The role of Big Data analytics capabilities in enhancing Lean Six Sigma efforts and the impact on organizational performance: An empirical study in Brazilian manufacturing

Manufacturing firms increasingly invest in Big Data Analytics (BDA) to support continuous improvement initiatives, yet many struggle to translate analytical capabilities into tangible performance gains. In particular, it remains unclear whether technological investments in analytics are sufficient, or whether managerial capabilities and process improvement practices are required to extract value from data-driven initiatives. Against this backdrop, this study examines how BDA capabilities—technological and managerial—relate to Lean Six Sigma (LSS) efforts and how LSS mediates the relationship between BDA and organizational performance. In addition, the study investigates whether data-driven culture and top management support condition the effectiveness of BDA in strengthening LSS practices. Survey data were collected from 184 professionals in Brazilian manufacturing firms with experience in both BDA and LSS and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that BDA management capability, rather than technological capability, supports LSS efforts, and that LSS acts as a key mechanism through which BDA management capability enhances operational and quality performance. No moderating effects were observed for data-driven culture or top management support. These results suggest that organizations seeking performance improvements from analytics should prioritize managerial routines, governance mechanisms, and the integration of data insights into structured LSS practices, rather than focusing solely on technological infrastructure.