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Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities

Author

Listed:
  • Rohit Agrawal

    (Indian Institute of Management)

  • Abhijit Majumdar

    (Indian Institute of Technology Delhi)

  • Anil Kumar

    (London Metropolitan University)

  • Sunil Luthra

    (Ch. Ranbir Singh State Institute of Engineering and Technology)

Abstract

Manufacturing firms often struggle to attain the optimum balance of environmental, economic, and social goals. Sustainable Manufacturing (SM) is one of the ways to balance the aforesaid aspects. Many disruptive technologies such as Artificial Intelligence (AI), blockchain, machine learning, the Internet of Things, and Big Data, are contributing immensely to the digitalisation in SM. This article aims to explore the trends of AI applications in SM during the period of 2010–2021 by conducting a systematic literature review and bibliometric and network analyses. Prominent research themes, namely sustainable scheduling, smart manufacturing and remanufacturing, energy consumption, sustainable practices and performances, and smart disassembly and recovery have been identified through network analysis. Content analysis of extant literature reveals that Genetic Algorithm (GA), Artificial Neural Network (ANN), and Fuzzy Logic are the most widely used AI techniques in SM. Potential future research directions like amalgamation of AI with Industry 4.0, use of hybrid AI systems, focus on social sustainability and use of emerging AI techniques (Deep learning, CNN etc.) have also been proposed. The intellectual map of AI in SM delineated in this article will be helpful for the researchers as well as industry practitioners in their future endeavours.

Suggested Citation

  • Rohit Agrawal & Abhijit Majumdar & Anil Kumar & Sunil Luthra, 2023. "Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities," Operations Management Research, Springer, vol. 16(4), pages 1720-1741, December.
    • Handle: RePEc:spr:opmare:v:16:y:2023:i:4:d:10.1007_s12063-023-00383-y
    DOI: 10.1007/s12063-023-00383-y
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