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

Lara Waltersmann, Steffen Kiemel, Julian Stuhlsatz, Alexander Sauer and Robert Miehe
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Lara Waltersmann: Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany
Steffen Kiemel: Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany
Julian Stuhlsatz: Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany
Alexander Sauer: Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany
Robert Miehe: Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany

Sustainability, 2021, vol. 13, issue 12, 1-26

Abstract: 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.

Keywords: sustainability; energy efficiency; material efficiency; water efficiency; greenhouse gas emissions; Green AI; AI; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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