Implementation of digital technologies for a circular economy and sustainability management in the manufacturing sector

There is growing consensus in literature and practice that digital technologies (DTs) can offer a wide range of potentials for implementing a circular economy in companies. However, empirical insights on how the potentials of different DTs are already realized across various industries are lacking. This study addresses this research gap through descriptive, hierarchical cluster and non-parametric analyses (Kruskal-Wallis tests and Spearman rank correlations) of the use of DTs for circular economy and sustainability management based on data collected in a structured telephone survey with 132 sustainability managers and CEOs of Austrian manufacturing companies. The paper shows for the first time the degree and stage of implementation of four key enabling DTs for a sustainable circular economy and 31 specific applications of those DTs across eight different industries. Of the four DTs, Internet of Things (IoT) technology is most widely implemented, followed by big data analytics, artificial intelligence (AI), and blockchain technology. However, their use in sustainability management is still very limited and is currently mainly in pilot phases. Of the 31 applications surveyed, IoT technology is most frequently used for collecting data from production processes, AI for predictive maintenance, big data analytics for demand forecasting, and blockchain technology for tracking product origins. Statistically significant differences were found in the degree of digitalization, in general, and for sustainability management between industries and company sizes. A strong positive correlation between the implementation of digital technologies in general and their use in a sustainability management context indicates synergies and spillover effects. The findings may help to tailor context-specific and purpose-driven strategies that selectively leverage the benefits of different DTs and supporting sustainability management effectively. Further research may identify scalable best practices, optimal enabling conditions, and environmental and social outcomes.