Skip to main content
Springer Nature Link
Log in

Artificial Intelligence Reshapes Supply Chain and Lean: Framework and Main Insights

  • Conference paper
  • First Online:
Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments (APMS 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 730))

  • 531 Accesses

Abstract

Smart manufacturing leverages various Industry 4.0 technologies to enhance the operational performance of manufacturing systems, strengthening their reconfigurability and resilience within Supply Chains (SC). Artificial Intelligence (AI) stands out as one of the most potent technologies driving these advancements, fundamentally reshaping SC behaviors. Concurrently, Lean production methodologies are employed to optimize manufacturing systems, synergizing with AI to reinforce technological efficiency. Recognizing the necessity of AI across diverse SC domains such as production, maintenance, logistics, supply, and quality, becomes imperative to comprehend its utility, applicability, and relevance. This paper tries to elucidate the role, limitations, and application framework of AI within Supply Chain Management (SCM), shedding light on its integration into contemporary SC paradigms. By updating existing knowledge on AI applications in SCM and sustaining Lean Processes, the authors address the evolving landscape of SC dynamics. Embracing this new era, the authors aim to delineate the boundaries of AI in SCM and articulate a framework for its prudent inclusion in the modern world. The Supply Chain Operations Reference (SCOR) model is utilized to contextualize the vast potential of AI, opening a rapidly evolving collaborative network facilitated by AI applications and decision support tools.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Perez, D., Samothrakis, S., Lucas, S.: Knowledge-based fast evolutionary MCTS for general video game playing. In: 2014 IEEE Conference on Computational Intelligence and Games, pp. 1–8. IEEE (2014)

    Google Scholar 

  2. Baryannis, G., Validi, S., Dani, S., Antoniou, G.: Supply chain risk management and artificial intelligence: state of the art and future research directions. Int. J. Prod. Res. 57(7), 2179–2202 (2019)

    Article  Google Scholar 

  3. Legg, S., Hutter, M.: Universal intelligence: a definition of machine intelligence. Mind. Mach. 17, 391–444 (2007)

    Article  Google Scholar 

  4. Snyder, L.V., Atan, Z., Peng, P., Rong, Y., Schmitt, A.J., Sinsoysal, B.: OR/MS models for supply chain disruptions: a review. IIE Trans. 48(2), 89–109 (2016)

    Article  Google Scholar 

  5. Zouggar Amrani, A., Ducq, Y.: Lean practices implementation in aerospace based on sector characteristics: methodology and case study. Prod. Planning & Control 31(16), 1313–1335 (2020)

    Article  Google Scholar 

  6. O’Connor, D., Cormican, K.: Leading from the middle: practices of team leaders on the front line of lean implementation. Int. J. Lean Six Sigma 13(2), 253–275 (2022)

    Article  Google Scholar 

  7. Sweeney, D., Nair, S., Cormican, K.: Scaling AI-based industry 4.0 projects in the medical device industry: an exploratory analysis Procedia Computer Sci. 219, 759–766 (2022)

    Google Scholar 

  8. Ganesh, A.D., Kalpana, P.: Future of artificial intelligence and its influence on supply chain risk management–a systematic review. Comput. Ind. Eng. 169, 108206 (2022)

    Article  Google Scholar 

  9. Schroeder, M., Lodemann, S.: A systematic investigation of the integration of machine learning into supply chain risk management. Logistics 5(3), 62 (2021)

    Article  Google Scholar 

  10. Belhadi, A., Kamble, S., Fosso Wamba, S., Queiroz, M.M.: Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. Int. J. Prod. Res. 60(14), 4487–4507 (2022)

    Article  Google Scholar 

  11. Hendriksen, C.: Artificial intelligence for supply chain management: disruptive innovation or innovative disruption? J. Supply Chain Manag. 59(3), 65–76 (2023)

    Article  Google Scholar 

  12. Wu, Y.C.J.: Effective lean logistics strategy for the auto industry. Int. J. Logistics Manage. 13(2), 19–38 (2002)

    Article  Google Scholar 

  13. Tortorella, G.L., Fettermann, D.: Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. Int. J. Prod. Res. 56(8), 2975–2987 (2018)

    Google Scholar 

  14. Dash, R., McMurtrey, M., Rebman, C., Kar, U.K.: Application of artificial intelligence in automation of supply chain management. J. Strategic Innovation and Sustainability 14(3) (2019)

    Google Scholar 

  15. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)

    Google Scholar 

  16. Pournader, M., Ghaderi, H., Hassanzadegan, A., Fahimnia, B.: Artificial intelligence applications in supply chain management. Int. J. Prod. Econ. 241, 108250 (2021)

    Article  Google Scholar 

  17. Ruiz-Hernández, D., Pinar-Pérez, J.M., Delgado-Gómez, D.: Multi-machine preventive maintenance scheduling with imperfect interventions: a restless bandit approach. Comput. Oper. Res. 119, 104927 (2020)

    Article  MathSciNet  Google Scholar 

  18. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., Fischl, M.: Artificial intelligence in supply chain management: a systematic literature review. J. Bus. Res. 122, 502–517 (2021)

    Article  Google Scholar 

  19. Fahle, S., Prinz, C., Kuhlenkötter, B.: Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application. Procedia CIRP 93, 413–418 (2020)

    Article  Google Scholar 

  20. Soni, N., Sharma, E.K., Singh, N., Kapoor, A.: Artificial intelligence in business: from research and innovation to market deployment. Procedia Computer Science 167, 2200–2210 (2020)

    Article  Google Scholar 

  21. Garibay, O., et al.: Six human-centered artificial intelligence grand challenges. Int. J. Human-Computer Interaction 39(3), 391–437 (2023)

    Article  Google Scholar 

  22. Kassa, A., Kitaw, D., Stache, U., Beshah, B., Degefu, G.: Artificial intelligence techniques for enhancing supply chain resilience: a systematic literature review, holistic framework, and future research. Computers & Industrial Eng. 109714 (2023)

    Google Scholar 

Download references

Acknowledgements

The research presented in this work was made possible with the support of project BEST at the University of Bordeaux, France. We extend our gratitude to the leaders of Work Package 2 for their encouragement and facilitation of this research, aimed at bolstering the research efforts within WP2 “Design and intelligent organizations”.

Disclosure of Interests.

The authors have no competing interests to declare that are relevant to the content of this article.

Author information

Authors and Affiliations

  1. IMS-Lab, University of Bordeaux, IMS, CNRS, 52 18, Bordeaux, France

    Anne Zouggar Amrani

  2. School of Engineering, University of Galway, Galway, Ireland

    Kathryn Cormican

  3. Sheffield University Management School, Sheffield, UK

    Diego Ruiz Hernandez

Authors
  1. Anne Zouggar Amrani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kathryn Cormican
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diego Ruiz Hernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anne Zouggar Amrani .

Editor information

Editors and Affiliations

  1. Chemnitz University of Technology, Chemnitz, Germany

    Matthias Thürer

  2. West Saxon University of Applied Sciences Zwickau, Zwickau, Germany

    Ralph Riedel

  3. ZF Friedrichshafen AG, Friedrichshafen, Germany

    Gregor von Cieminski

  4. Tecnológico de Monterrey, Mexico City, Mexico

    David Romero

Rights and permissions

Reprints and permissions

Copyright information

© 2024 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Amrani, A.Z., Cormican, K., Hernandez, D.R. (2024). Artificial Intelligence Reshapes Supply Chain and Lean: Framework and Main Insights. In: Thürer, M., Riedel, R., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments. APMS 2024. IFIP Advances in Information and Communication Technology, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-031-71629-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-71629-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-71628-7

  • Online ISBN: 978-3-031-71629-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships