Mapping and prioritizing industry 4.0 technologies: an analysis based on patent and scientific publication networks

Abstract Industry 4.0 has rapidly evolved since its introduction in 2011, driving global initiatives and promoting the diffusion of enabling technologies, such as IoT, cyber-physical systems, and artificial intelligence. Despite its well-documented benefits, firms face increasing challenges in selecting and integrating multiple​ technologies, particularly amid expanding technological options and uncertainty in implementation pathways. Knowing about this challenge, this study develops a systematic, data-driven methodology to support the prioritization of Industry 4.0 enabling technologies. Drawing on network theory, it analyzes data from scientific publications and patents​ by constructing proximity matrices. A composite index derived from network metrics, combined with a portfolio distance measure, is used to rank and prioritize technologies. The results are benchmarked against real industrial adoption data, highlighting similarities and differences between the approaches, while providing insights on the methodology’s strengths and limitations. Provided that further empirical validation is achieved, the proposed framework holds considerable potential to support managerial decision-making, as well as to be scaled to other technological fields.