@incollection{BraunerVervierBrillowskietal.2022,
  author    = {Brauner, Philipp and Vervier, Luisa and Brillowski, Florian and Dammers, Hannah and Steuer-Dankert, Linda and Schneider, Sebastian and Baier, Ralph and Ziefle, Martina and Gries, Thomas and Leicht-Scholten, Carmen and Mertens, Alexander and Nagel, Saskia K.},
  title     = {Organization Routines in Next Generation Manufacturing},
  series = {Forecasting Next Generation Manufacturing},
  booktitle = {Forecasting Next Generation Manufacturing},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-07734-0},
  doi       = {10.1007/978-3-031-07734-0_5},
  pages     = {75 -- 94},
  year      = {2022},
  abstract  = {Next Generation Manufacturing promises significant improvements in performance, productivity, and value creation. In addition to the desired and projected improvements regarding the planning, production, and usage cycles of products, this digital transformation will have a huge impact on work, workers, and workplace design. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these changes, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the organization dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Specifically, we highlight seven areas in which the digital transformation of production will change how we work, how we organize the work within a company, how we evaluate these changes, and how employment and labor rights will be affected across company boundaries. The experts are unsure whether the use of collaborative robots in factories will replace traditional robots by 2030. They believe that the use of hybrid intelligence will supplement human decision-making processes in production environments. Furthermore, they predict that artificial intelligence will lead to changes in management processes, leadership, and the elimination of hierarchies. However, to ensure that social and normative aspects are incorporated into the AI algorithms, restricting measurement of individual performance will be necessary. Additionally, AI-based decision support can significantly contribute toward new, socially accepted modes of leadership. Finally, the experts believe that there will be a reduction in the workforce by the year 2030.},
  language  = {en}
}
@incollection{HinkeVervierBrauneretal.2022,
  author    = {Hinke, Christian and Vervier, Luisa and Brauner, Philipp and Schneider, Sebastian and Steuer-Dankert, Linda and Ziefle, Martina and Leicht-Scholten, Carmen},
  title     = {Capability configuration in next generation manufacturing},
  series = {Forecasting next generation manufacturing : digital shadows, human-machine collaboration, and data-driven business models},
  booktitle = {Forecasting next generation manufacturing : digital shadows, human-machine collaboration, and data-driven business models},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-07733-3},
  doi       = {10.1007/978-3-031-07734-0_6},
  pages     = {95 -- 106},
  year      = {2022},
  abstract  = {Industrial production systems are facing radical change in multiple dimensions. This change is caused by technological developments and the digital transformation of production, as well as the call for political and social change to facilitate a transformation toward sustainability. These changes affect both the capabilities of production systems and companies and the design of higher education and educational programs. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these concepts, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the capabilities dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Specifically, we discuss the benefits of capturing expert knowledge and making it accessible to newcomers, especially in highly specialized industries. The experts argue that in order to cope with the challenges and circumstances of today's world, students must already during their education at university learn how to work with AI and other technologies. This means that study programs must change and that universities must adapt their structural aspects to meet the needs of the students.},
  language  = {en}
}