IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i12p6689-d573901.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/12/6689/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/12/6689/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anna Lütje & Martina Willenbacher & Martin Engelmann & Christian Kunisch & Volker Wohlgemuth, 2020. "Exploring the System Dynamics of Industrial Symbiosis (IS) with Machine Learning (ML) Techniques—A Framework for a Hybrid-Approach," Progress in IS, in: Rüdiger Schaldach & Karl-Heinz Simon & Jens Weismüller & Volker Wohlgemuth (ed.), Advances and New Trends in Environmental Informatics, pages 117-130, Springer.
    2. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    3. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    4. Tsiliyannis, Christos Aristeides, 2018. "Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age," European Journal of Operational Research, Elsevier, vol. 271(2), pages 474-489.
    5. Nicola Jones, 2018. "How to stop data centres from gobbling up the world’s electricity," Nature, Nature, vol. 561(7722), pages 163-166, September.
    6. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Henry Ekwaro-Osire & Dennis Bode & Jan-Hendrik Ohlendorf & Klaus-Dieter Thoben, 2025. "Manufacturing process energy consumption modeling: a methodology to identify the most appropriate model," Journal of Intelligent Manufacturing, Springer, vol. 36(8), pages 5673-5693, December.
    3. Luo, Qingfeng & Wang, Jingyuan, 2025. "The impact of artificial intelligence development on embodied carbon emissions: Perspectives from the production and consumption sides," Energy Policy, Elsevier, vol. 199(C).
    4. Zhu, Huayou & Bao, Weiping & Yu, Guojun, 2024. "How can intelligent manufacturing lead enterprise low-carbon transformation? Based on China's intelligent manufacturing demonstration projects," Energy, Elsevier, vol. 313(C).
    5. 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.
    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. Cagno, Enrico & Accordini, Davide & Thollander, Patrik & Andrei, Mariana & Hasan, A S M Monjurul & Pessina, Sonia & Trianni, Andrea, 2025. "Energy management and industry 4.0: Analysis of the enabling effects of digitalization on the implementation of energy management practices," Applied Energy, Elsevier, vol. 390(C).
    8. 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.
    9. Yuxuan Wang & Ze Tian & Xiaodong Jing & Mengyao Li, 2025. "Development Dynamics and Influencing Factors of China’s Agricultural Green Ecological Efficiency Based on an Evaluation Model Incorporating Ecosystem Service Value and Carbon Emissions," Sustainability, MDPI, vol. 17(18), pages 1-31, September.
    10. 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).
    11. Cevik, Nuket Kırcı & Cevik, Emrah I. & Destek, Mehmet Akif & Bugan, Mehmet Fatih & Manga, Müge, 2024. "Unleashing power of financial technologies on mineral productivity in G-20 countries," Resources Policy, Elsevier, vol. 90(C).
    12. Tengfei Shen & Alina Badulescu, 2025. "Generative AI and Sustainable Performance in Manufacturing Firms: Roles of Innovations and AI Regulation," Sustainability, MDPI, vol. 17(19), pages 1-23, September.
    13. 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.
    14. 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.
    15. Juan Yu & Weihong Xie & Xiuyi Zhao & Zhongshun Li & Liang Guo, 2025. "Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulations," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-17, December.
    16. 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.
    17. Zhen Wang & Hongwen Jia & Jiale Wu, 2025. "The Impact of Urban Digital Intelligence Transformation on Corporate Carbon Performance: Evidence from China," Sustainability, MDPI, vol. 17(12), pages 1-26, June.
    18. Hui Huang & Jing Yang & Changman Ren, 2025. "The Impact and Mechanisms of Artificial Intelligence on Green Economic Efficiency: Empirical Evidence from China’s GTFP Improvement," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(6), pages 18353-18387, December.
    19. Yin, Hua & Yin, Xieyu & Wen, Fenghua, 2025. "Artificial intelligence and climate risk: A double machine learning approach," International Review of Financial Analysis, Elsevier, vol. 103(C).
    20. 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).
    21. Lin, Boqiang & Zhu, Yitong, 2025. "The impact of artificial intelligence policy on green innovation of firms," Energy Economics, Elsevier, vol. 148(C).
    22. 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.
    23. 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.
    24. Hui Tian & Jiaqi Qin & Chaoyin Cheng & Sohail Ahmad Javeed & Tiansi Chu, 2024. "Towards low‐carbon sustainable development under Industry 4.0: The influence of industrial intelligence on China's carbon mitigation," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(1), pages 455-480, February.
    25. Rame, Rame & Purwanto, Purwanto & Sudarno, Sudarno, 2024. "Industry 5.0 and sustainability: An overview of emerging trends and challenges for a green future," Innovation and Green Development, Elsevier, vol. 3(4).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefano Bianchini & Giacomo Damioli & Claudia Ghisetti, 2023. "The environmental effects of the “twin” green and digital transition in European regions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 877-918, April.
    2. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    3. Lee, Chien-Chiang & Zou, Jinyang & Chen, Pei-Fen, 2025. "The impact of artificial intelligence on the energy consumption of corporations: The role of human capital," Energy Economics, Elsevier, vol. 143(C).
    4. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    5. Abdulmajeed Faihan Alotaibi, 2024. "Ethical Guidelines of Integrating Artificial Intelligence in Healthcare in Alignment with Sustainable Development," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 59(4), pages 51710-51716, November.
    6. Chen, Zhan-Ming & Xiong, Qiyang & Duan, Jiahui & Ma, Jianhong & Chen, Zhuo & Guo, Shan, 2025. "AI carbon footprint in China sets to double post-2030 carbon peaking," Energy Economics, Elsevier, vol. 150(C).
    7. Raghu Raman & Hiran H Lathabai & Santanu Mandal & Payel Das & Tavleen Kaur & Prema Nedungadi, 2024. "ChatGPT: Literate or intelligent about UN sustainable development goals?," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-27, April.
    8. Basma Hamrouni & Abdelhabib Bourouis & Ahmed Korichi & Mohsen Brahmi, 2021. "Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability," Sustainability, MDPI, vol. 13(17), pages 1-28, September.
    9. White, Katherine & Cakanlar, Aylin & Sethi, Shakti & Trudel, Remi, 2025. "The past, present, and future of sustainability marketing: How did we get here and where might we go?," Journal of Business Research, Elsevier, vol. 187(C).
    10. David Mhlanga, 2022. "Human-Centered Artificial Intelligence: The Superlative Approach to Achieve Sustainable Development Goals in the Fourth Industrial Revolution," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    11. Ying-Hui Shao & Yan-Hong Yang & Han-Xian Zhou & Wei-Xing Zhou, 2025. "Dynamic spillovers and investment strategies across artificial intelligence ETFs, artificial intelligence tokens, and green markets," Papers 2503.01148, arXiv.org, revised Jun 2025.
    12. Denicolai, Stefano & Zucchella, Antonella & Magnani, Giovanna, 2021. "Internationalization, digitalization, and sustainability: Are SMEs ready? A survey on synergies and substituting effects among growth paths," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    13. Costa, Alessandra & Crupi, Antonio & Cesaroni, Fabrizio & Abbate, Tindara, 2025. "Exploring the role of artificial intelligence in addressing sustainable development. A semantic analysis of AI patents," Technovation, Elsevier, vol. 148(C).
    14. Solène Guenat & Phil Purnell & Zoe G. Davies & Maximilian Nawrath & Lindsay C. Stringer & Giridhara Rathnaiah Babu & Muniyandi Balasubramanian & Erica E. F. Ballantyne & Bhuvana Kolar Bylappa & Bei Ch, 2022. "Meeting sustainable development goals via robotics and autonomous systems," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    15. Daniele Giordino & Elisa Ballesio & Nourah Alshaghdali & Dhruv Galgotia, 2026. "The relationship between organizational focus on AI, financial growth and sustainable development: Evidence from Europe," Post-Print hal-05433094, HAL.
    16. Zhong, Wenli & Liu, Yang & Dong, Kangyin & Ni, Guohua, 2024. "Assessing the synergistic effects of artificial intelligence on pollutant and carbon emission mitigation in China," Energy Economics, Elsevier, vol. 138(C).
    17. Zu, Xu & Ni, Guangxian & Hu, Ruifeng, 2025. "AI technology innovation, knowledge management and corporate environmental sustainability: Evidence from Chinese patent data," Technology in Society, Elsevier, vol. 83(C).
    18. Li, Chao & Zhang, Yuhan & Li, Xiang & Hao, Yanwei, 2024. "Artificial intelligence, household financial fragility and energy resources consumption: Impacts of digital disruption from a demand-based perspective," Resources Policy, Elsevier, vol. 88(C).
    19. Burhan Akpınar & Müzeyyen Barut & Esra Nur Akpınar & Hasan Celal Balıkçı, 2025. "Lifelong Learning Supported by Artificial Intelligence and Technology for Sustainable Development Goals: An OECD Perspective," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(5), pages 7826-7843, October.
    20. Muhammad Tanveer & Shafiqul Hassan & Amiya Bhaumik, 2020. "Academic Policy Regarding Sustainability and Artificial Intelligence (AI)," Sustainability, MDPI, vol. 12(22), pages 1-13, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6689-:d:573901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.