Implementation of a Sustainable Framework for Process Optimization Through the Integration of Robotic Process Automation and Big Data in the Evolution of Industry 4.0

Abstract

This study explores the integration of Robotic Process Automation (RPA) and Big Data within a sustainable framework for process optimization in the context of Industry 4.0. As industries strive to enhance operational efficiency while maintaining sustainability, the need for innovative solutions has become crucial. The research applies the PICO methodology (Population, Intervention, Comparison, Outcome) to assess the impact of combining these technologies on process optimization and sustainability. Through a real-world case study, the study demonstrates that the integration of RPA and Big Data significantly reduces execution times, minimizes operational errors, and promotes sustainable business practices. The results show that the combined framework not only enhances efficiency but also contributes to lower economic, environmental, and social impacts. The findings validate the research hypotheses, proving that the proposed framework fosters a balance between technological advancement and sustainability. This study provides valuable insights into the potential of Industry 4.0 technologies to drive both operational efficiency and corporate responsibility, offering a novel approach for industries seeking to embrace digital transformation while achieving long-term sustainability. The research contributes to the growing body of knowledge on the synergy between RPA, Big Data, and sustainability in industrial contexts.