Relations between Industry 4.0 and Lean Six Sigma: Analysis of Big Data Analytics and Organizational Factors

Abstract

Industry 4.0 has profoundly transformed the production environment, imposing new challenges on organizations, such as the management of large volumes of data, the need for mass customization, and the pursuit of greater operational efficiency. In this context, the integration between Big Data Analytics (BDA) and Lean Six Sigma (LSS) emerges as a strategic pathway to enhance continuous improvement, promote evidence-based decision-making, and sustain organizational performance. The main objective of this article is to investigate the impact of BDA capabilities, subdivided into technological and managerial, on LSS efforts, while also examining whether LSS acts as a mediator between BDA and organizational outcomes (financial, operational, and quality). The moderating roles of data-driven culture (DDC) and top management support (TMS) in the relationship between BDA and LSS are also analyzed. The research was conducted in two stages. First, a Systematic Literature Review (SLR) was developed, which revealed that although scientific production on the topic is increasing, it remains recent and predominantly conceptual. The SLR highlighted BDA as the technology with the greatest impact on LSS by providing real-time data and advanced analytics for tools such as SPC, VSM, Poka-Yoke, and VoC. It also identified that organizational factors such as leadership, culture, and training are critical to the successful integration of LSS and Industry 4.0. In the second stage, a survey was conducted with 184 professionals from the Brazilian manufacturing industry, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical results demonstrate that managerial BDA capabilities exert a positive and significant effect on LSS efforts, whereas technological capabilities alone do not produce a relevant impact. It was further found that LSS acts as an indispensable mediator for BDA to contribute to operational and quality performance, with no direct impact on financial performance. Conversely, neither DDC nor TMS showed significant moderation. These findings extend the literature by providing novel quantitative evidence in a developing country, demonstrating that the value of BDA does not lie in technological infrastructure itself, but in its capacity to be translated into continuous improvement practices mediated by LSS. From a managerial perspective, the results reinforce the importance of investing in managerial analytics capabilities, strategically aligned with LSS, in order to transform data into sustainable performance gains.