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Review

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany
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Academic Editors: Vinit Prida and Wiebke Reim
Sustainability 2021, 13(12), 6689; https://doi.org/10.3390/su13126689
Received: 30 April 2021 / Revised: 8 June 2021 / Accepted: 9 June 2021 / Published: 12 June 2021
(This article belongs to the Special Issue AI and Machine Learning towards Circular and Sustainable Industry)
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 the current 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. View Full-Text
Keywords: sustainability; energy efficiency; material efficiency; water efficiency; greenhouse gas emissions; Green AI; AI; machine learning sustainability; energy efficiency; material efficiency; water efficiency; greenhouse gas emissions; Green AI; AI; machine learning
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MDPI and ACS Style

Waltersmann, L.; Kiemel, S.; Stuhlsatz, J.; Sauer, A.; Miehe, R. Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review. Sustainability 2021, 13, 6689. https://doi.org/10.3390/su13126689

AMA Style

Waltersmann L, Kiemel S, Stuhlsatz J, Sauer A, Miehe R. Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review. Sustainability. 2021; 13(12):6689. https://doi.org/10.3390/su13126689

Chicago/Turabian Style

Waltersmann, Lara, Steffen Kiemel, Julian Stuhlsatz, Alexander Sauer, and Robert Miehe. 2021. "Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review" Sustainability 13, no. 12: 6689. https://doi.org/10.3390/su13126689

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