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Transdisciplinary Engineering (TE) is an emerging area of research able to evolve traditional engineering approaches by transcending the technical disciplines. It can be successfully applied in different fields, by combining natural sciences, applied sciences, social sciences, and humanities to achieve a higher level of comprehension and awareness of the context in which industrial products, processes, systems, and services will be implemented and experienced by users (Borsato et al. 2016). Research in TE also incorporates social science methodologies to acquire knowledge about users and context, and solve ill-defined, socially relevant problems. Based on recent evidence, it can be stated that numerous engineering problems can be characterised as ill-defined and socially relevant, too (Wognum et al. 2019). Industry 4.0 is today a well-known paradigm that pushes the vision of a smart factory based on intelligent manufacturing. The intelligence of machines is mainly enabled by networking production systems and real-time process control via cyber-physical systems (CPSs) and Internet-of-Things (IoT) to have greater productivity through resource efficiency. However, a lot of aspects need to be included to fully achieve this challenging objective, from selection of sensors and smart components to efficient and feasible data collection, proper information system architecture to reliable data analysis, to knowledge representation and data requirements definition, until production line management, also including the need for people with the right type of knowledge and interaction with humans. Indeed, intelligent manufacturing is not just about machines, as we can think in a general way, but also about people and product-process knowledge management, merging the physical and digital worlds (Zhong et al. 2017). As a matter of fact, creating a smart factory is a complex problem. To support a fully sustainable development, based on resource-efficient production systems, promoting safety, innovation, and economy, smart factories need to exploit digital trends as well as users’ active participatory and collaborative processes (Peruzzini et al. 2020). A vertical networking of smart production systems is required as well as a horizontal networking of smart logistics, production, marketing and smart services, able to generate global value-creation networks, including integration of business partners and customers, and new business and cooperation models across companies and countries. In smart factories, machines are becoming more and more digitised and technologically advanced. In this context, new approaches and methodologies are required to bridge the gaps between technical and social sciences. TE approaches can help to bring the intelligence into the shop floor to provide factories with flexible and adaptive behaviours (e.g. self-steering or continuous improvement teams). Moreover, social sciences are necessary to include people from practice and relate their needs and the system features at different levels (considering the users, the context, the machine, and the interface). Next to different methodologies, novel technologies like virtual tools are necessary to anticipate critical conditions and to envisage possible solutions. In addition, proper training is needed for people to understand the new processes and to be able to work in the new environment and collaborate with others. The new mindset needs to be incorporated on all levels in the organisation, from top management to the work floor. This special issue is aligned with these developments and challenges. It includes invited papers selected from contributions to the 27 International Conference on Transdisciplinary Engineering held online from 1 to 10 July 2020, hosted by the Warsaw University of technology, Poland (Pokojski et al. 2020) and supported by the International Society for Transdisciplinary Engineering (ISTE). The authors come from traditional industrial countries, such as Italy, Germany and Poland in Europe, and more recently industrialized countries INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2022, VOL. 35, NO. 1, 1–3 https://doi.org/10.1080/0951192X.2022.2028369