TY  - CHAP
A1  - Brauner, Philipp
A1  - Vervier, Luisa
A1  - Brillowski, Florian
A1  - Dammers, Hannah
A1  - Steuer-Dankert, Linda
A1  - Schneider, Sebastian
A1  - Baier, Ralph
A1  - Ziefle, Martina
A1  - Gries, Thomas
A1  - Leicht-Scholten, Carmen
A1  - Mertens, Alexander
A1  - Nagel, Saskia K.
T1  - Organization Routines in Next Generation Manufacturing
T2  - Forecasting Next Generation Manufacturing
N2  - 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.
Y1  - 2022
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/10213
SN  - 978-3-031-07734-0
SP  - 75
EP  - 94
PB  - Springer
CY  - Cham
ER  -