Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.
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.
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