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Integrating Artificial Intelligence into the Supply Chain in Order to Enhance Sustainable Production—A Systematic Literature Review

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  • Justyna Patalas-Maliszewska

    (Institute of Mechanical Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland)

  • Małgorzata Szmołda

    (State Archives in Zielona Góra, 65-762 Zielona Góra, Poland)

  • Hanna Łosyk

    (Institute of Mechanical Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland)

Abstract

Nowadays, integrating Artificial Intelligence (AI) into supply chains (SCs) is a great challenge in research and for manufacturing managers. The main goal of this study is to determine the role of AI in the context of the new SCs, according to the concept of Industry 5.0. in order to improve the level of sustainable production. The research was based on a systematic analysis of the scientific literature and application of the PRISMA methodology. Due to the relatively new vision of introducing AI into SC, it was decided to analyse the years 2021–2024. A total of 1181 research articles were identified in Science Direct, Springer and the Willey Online Library that combined AI-based methods and tools that support SCs in order to identify the impacts and challenges of integrating AI in SCs in the context of sustainable production (SP). In this study, 48 items were then analysed in detail. The results achieved highlighted the main AI-based tools applied in SCs and, secondly, revealed the main benefits of this integration for manufacturing in the following areas of manufacturing: predictive maintenance, production planning and customer relationships. The findings of our study revealed the main challenges and directions: (1) integrating digitalisation and green SP in order to build resilience to the SP, (2) create a sustainable work environment, (3) and develop a sustainable and advanced architecture for relationships with customers.

Suggested Citation

  • Justyna Patalas-Maliszewska & Małgorzata Szmołda & Hanna Łosyk, 2024. "Integrating Artificial Intelligence into the Supply Chain in Order to Enhance Sustainable Production—A Systematic Literature Review," Sustainability, MDPI, vol. 16(16), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7110-:d:1459321
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    References listed on IDEAS

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    1. Dmitry Ivanov, 2018. "OR/MS Methods for Structural Dynamics in Supply Chain Risk Management," International Series in Operations Research & Management Science, in: Structural Dynamics and Resilience in Supply Chain Risk Management, chapter 0, pages 115-159, Springer.
    2. Dmitry Ivanov, 2024. "Two views of supply chain resilience," International Journal of Production Research, Taylor & Francis Journals, vol. 62(11), pages 4031-4045, June.
    3. Dmitry Ivanov, 2018. "Structural Dynamics and Resilience in Supply Chain Risk Management," International Series in Operations Research and Management Science, Springer, number 978-3-319-69305-7, March.
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    1. Xiaofei Wang & Shaowen Zhan & Longlong Liu & Peng Zhang, 2025. "Research on the Green and Low-Carbon Development Path of Digital Intelligence Empowering Enterprises in Manufacturing Industry," Sustainability, MDPI, vol. 17(6), pages 1-23, March.
    2. Maria Antoniadou, 2024. "Integrating Lean Management and Circular Economy for Sustainable Dentistry," Sustainability, MDPI, vol. 16(22), pages 1-25, November.
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    4. Lorenzo Becchi & Elisa Belloni & Marco Bindi & Matteo Intravaia & Francesco Grasso & Gabriele Maria Lozito & Maria Cristina Piccirilli, 2024. "A Computationally Efficient Rule-Based Scheduling Algorithm for Battery Energy Storage Systems," Sustainability, MDPI, vol. 16(23), pages 1-21, November.

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