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

Author

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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)

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.

Suggested Citation

  • Lara Waltersmann & Steffen Kiemel & Julian Stuhlsatz & Alexander Sauer & Robert Miehe, 2021. "Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6689-:d:573901
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    References listed on IDEAS

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    Cited by:

    1. Philip Krummeck & Yagmur Damla Dokur & Daniel Braun & Steffen Kiemel & Robert Miehe, 2022. "Designing Component Interfaces for the Circular Economy—A Case Study for Product-As-A-Service Business Models in the Automotive Industry," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    2. Abou-Foul, Mohamad & Ruiz-Alba, Jose L. & López-Tenorio, Pablo J., 2023. "The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective," Journal of Business Research, Elsevier, vol. 157(C).
    3. Robert Miehe & Lorena Buckreus & Steffen Kiemel & Alexander Sauer & Thomas Bauernhansl, 2021. "A Conceptual Framework for Biointelligent Production—Calling for Systemic Life Cycle Thinking in Cellular Units," Clean Technol., MDPI, vol. 3(4), pages 1-14, December.
    4. Xiaozhong Li & Jun Ling, 2023. "The Impact of Manufacturing Intelligence on Green Development Efficiency: A Study Based on Chinese Data," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    5. Steffen Kiemel & Chantal Rietdorf & Maximilian Schutzbach & Robert Miehe, 2022. "How to Simplify Life Cycle Assessment for Industrial Applications—A Comprehensive Review," Sustainability, MDPI, vol. 14(23), pages 1-26, November.
    6. João M. R. C. Fernandes & Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review," Sustainability, MDPI, vol. 14(10), pages 1-34, May.
    7. Li, Juan & Ma, Shaoqi & Qu, Yi & Wang, Jiamin, 2023. "The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China," Resources Policy, Elsevier, vol. 82(C).
    8. Yousra El kihel & Ali El kihel & El Mahdi Bouyahrouzi, 2022. "Contribution of Maintenance 4.0 in Sustainable Development with an Industrial Case Study," Sustainability, MDPI, vol. 14(17), pages 1-26, September.
    9. Robert Miehe & Matthias Finkbeiner & Alexander Sauer & Thomas Bauernhansl, 2022. "A System Thinking Normative Approach towards Integrating the Environment into Value-Added Accounting—Paving the Way from Carbon to Environmental Neutrality," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    10. Víctor Hugo Arredondo-Méndez & Lorena Para-González & Carlos Mascaraque-Ramírez & Manuel Domínguez, 2021. "The 4.0 Industry Technologies and Their Impact in the Continuous Improvement and the Organizational Results: An Empirical Approach," Sustainability, MDPI, vol. 13(17), pages 1-24, September.
    11. Wang, Jianda & Wang, Kun & Dong, Kangyin & Zhang, Shiqiu, 2023. "Assessing the role of financial development in natural resource utilization efficiency: Does artificial intelligence technology matter?," Resources Policy, Elsevier, vol. 85(PA).

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