Industry 4.0 and lean for sustainability performance

Abstract

Industrial activity is one of the main responsible for environmental, social, and economic impacts, which leads industries to seek new ways to manage their processes. In this scenario, Lean has become a strong ally of the industrial sector since the application of the Lean socio-technical system can improve sustainability performance. In addition, the sector is undergoing important technological changes amid Industry 4.0 (I4.0) and understanding how these approaches interact is of great interest to academics and practitioners. Therefore, this research is dedicated to identifying and proposing ways to integrate Lean and I4.0 and analyze the effect of these relationships on sustainability performance. For this, two Systematic Literature Reviews (SLR) were conducted. The first aimed at analyzing the integration between the three themes, Lean, Sustainability, and I4.0, and the second shows the relationship between Lean and I4.0 to support sustainability. The second analysis aims to deepen how relationships and support can take place, which was strengthened by cases study. The results show that the industries can benefit from the integration between Industry 4.0 Technologies (I4T) and Lean Practices (LP) to exploit the technological and human potential and support the operational system in improving sustainability measures. The initial evidence suggests a greater potential for integration between I4T such as the Internet of Things (IoT) and Big Data Analytics (BDA), with Lean Technical Practices (LTP) such as Total Preventive Maintenance (TPM) and Just-in-Time (JIT), and Lean Social Practices (LSP) such as Continuous Improvement, Customer Involvement, and Supplier Partnership. In this sense, Economic Performance indicators are the most favored, with little evidence in the literature of improvements in environmental and social performance indicators resulting from the integration between I4T and LP. The cases study confirm the findings of the literature and add evidence of how the main support relationships occur in an industrial environment, in addition to addressing negative aspects of integration. The results of the literature guided the construction of a model conceptual model tested through survey research with the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. The survey research was developed to assess the direct and indirect effects of Big Data Analytics Capabilities (BDAC) on economic, environmental, and social performance, in the presence of Lean Socio-Technical Practices as mediating variables. The results confirm the direct influence of BDAC on Social Performance and identify Lean Technical Practices as significant mediating variables that act as a catalyst to boost the indirect impacts of BDAC on Economic Performance. This comprehensive research can help researchers and practitioners fully understand and benefit from the integration of I4T, particularly BDAC, with the LP while managing sustainability issues.