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Sustainable production and the role of digital twins - Basic reflections and perspectives

: Miehe, Robert; Waltersmann, Lara; Sauer, Alexander; Bauernhansl, Thomas

Volltext ()

Journal of advanced manufacturing and processing 3 (2021), Nr.2, Art. e10078, 21 S.
ISSN: 2637-403X
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IPA ()
advanced manufacturing; Nachhaltigkeit; Digitaler Zwilling

Sustainable production is essential for the future of the global economy. Despite the publication of its baseline vision over 30 years ago and the resulting diversity of interpretations and subdisciplines in engineering and social sciences, the progress of the approach in industrial practice remains marginal. This is mainly due to the fact that the discipline has not yet succeeded to realize the magnitude of the rethinking necessary of its very own perception as a whole. Existing definitions of sustainable production presented to date are thus only partly consistently derived from the baseline concept. Meanwhile, digitalization provides an increasing number of technologies that offer a new perspective on sustainable production. This especially applies to the concept of digital twins. Recent studies, thus, address their role in the context of sustainable production by analyzing its contribution to existing sustainability related methods as well as technical challenges on a microeconomic level (bottom-up approach). Although these approaches provide concrete requirements for technical deployment, it is highly questionable how they will contribute to sustainable production as a whole. In this paper, we choose a top-down approach to discuss the role of digital twins in the context of sustainable production. Based on fundamental reflections on the baseline concept of sustainability, we advocate a reorientation of production within the framework of planetary boundaries. Thereupon, we discuss the role of digital twins and digital threads and provide a number of requirements that future R&D needs to address for a future sustainability-oriented data-driven monitoring and regulation of production.