Skip to main content
SpringerLink
Log in

Artificial Intelligence Applications in the Global Supply Chain: Benefits and Challenges

  • Conference paper
  • First Online:
International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 712))

  • 327 Accesses

Abstract

Organizations are seeking to improve their management systems in a period of economic industrialization in which the environment is becoming increasingly competitive and widespread. In the industry 4.0 era, most businesses strive to be reactive and agile by incorporating new technologies such as artificial intelligence (AI) in supply chain management.

Nowadays, Supply chains (SC) differ from those of just a few decades ago, and they are evolving in a highly competitive economic system. Dynamic supply chain processes require a technology that can cope with their increasing complexities. Moreover, quality user stories, budget control, and a firm’s agility in the light of economic opportunities and uncertainties are all dependent on supply chains. The only way of increasing operational efficiency is to use the right technology at the right time.

In recent years, several functional supply chain applications based on artificial intelligence (AI) have emerged. Artificial intelligence has the potential to significantly improve the performance of the overall economy. However, it could have a greater influence by serving as a method of invention that can revolutionize the nature of the process of innovation in SC and R&D institutions.

The main purpose of this paper is to identify the critical role of AI technology in providing greater flexibility and control to supply chain processes, and also to bring light to SC reaction between its benefits and challenges.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Collin, J., et al.: How to design the right supply chains for your customers. Supply Chain Manag. 14, 411–417 (2016)

    Google Scholar 

  2. Mentzer, J.T., et al.: Defining supply chain management. J. Bus. Logist. (2001)

    Google Scholar 

  3. Ni, D., Xiao, Z., Lim, M.K.: A systematic review of the research trends of machine learning in supply chain management. Int. J. Mach. Learn. Cybern. 11, 1463–1482 (2019). Springer

    Google Scholar 

  4. Huang, B., Huan, Y., Xu, L., Zheng, L., Zou, Z.: Automated trading systems statistical and machine learning methods and hardware implementation: a survey. Enterp. Inf. Syst. 13(1), 132–144 (2019)

    Article  Google Scholar 

  5. Huang, C., Cai, H., Xu, L., Xu, B., Gu, Y., Jiang, L.: Data-driven ontology generation and evolution towards intelligent service in manufacturing systems. Future Gener. Comput. Syst. 101, 197–207 (2019)

    Article  Google Scholar 

  6. Rajkomar, E., et al.: Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1(1), 1–10 (2018)

    Article  Google Scholar 

  7. Xu, L., Tan, W., Zhen, H., Shen, W.: An approach to enterprise process dynamic modeling supporting enterprise process evolution. Inf. Syst. Front. 10(5), 611–624 (2008)

    Article  Google Scholar 

  8. Shi, Z., et al.: MSMiner—a developing platform for OLAP. Decis. Support Syst. 42(4), 2016–2028 (2007)

    Article  Google Scholar 

  9. Zhang, C., Xu, X., Chen, H.: Theoretical foundations and applications of cyberphysical systems. J. Libr. Hi Tech 38(1), 95–104 (2019)

    Article  Google Scholar 

  10. Bostrom, N., Yudkowsky, E.: The ethics of artificial intelligence. Cambridge Handb. Artif. Intell. 1, 316–334 (2014)

    Article  Google Scholar 

  11. Habimana, O., Li, Y., Li, R., Gu, X., Yu, G.: Sentiment analysis using deep learning approaches: an overview. Sci. China Inf. Sci. 63, 1–36 (2020). Springer

    Google Scholar 

  12. Lopes, V., Alexandre, L.A.: An overview of blockchain integration with robotics and artificial intelligence. arXiv preprint arXiv:1810.00329 (2018). arxiv.org

  13. Nilsson, N.J.: Principles of Artificial Intelligence. Morgan Kaufmann Publishers Inc., Palo Alto (2014)

    MATH  Google Scholar 

  14. Erhan, D., Courville, A., Bengio, Y., Vincent, P.: Why does unsupervised pre-training help deep learning?. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. In: JMLR Workshop and Conference Proceedings, pp. 201–208 (2010)

    Google Scholar 

  15. Hu, R.Q., Hanzo, L.: Twin-timescale artificial intelligence aided mobility-aware edge caching and computing in vehicular networks. IEEE Trans. Veh. Technol. 68(4), 3086–3099 (2019)

    Article  Google Scholar 

  16. Chen, X.W., Lin, X.: Big data deep learning: challenges and perspectives. IEEE Access 2, 514–525 (2014)

    Article  Google Scholar 

  17. Zhang, C.: Research on the economical influence of the difference of regional logistics developing level in China. J. Industr. Integr. Manag. 05(02), 205–223 (2020)

    Article  Google Scholar 

  18. Bughin, J., Hzan, E., Ramaswamy, S., Chui, M., et al.: Artificial Intelligence: The Next Digital Frontier? McKinsey Global Institute, Washington, DC (2017)

    Google Scholar 

  19. Burgess, A.: AI in action. In: The Executive Guide to Artificial Intelligence. Palgrave Macmillan, Cham (2018)

    Google Scholar 

  20. Kusiak, A.: Smart manufacturing, Int. J. Prod. Res. 56, 508–517 (2018)

    Google Scholar 

  21. Martin, C., Leurent, H.: Technology and innovation for the future of production: accelerating value creation. WEF (2017)

    Google Scholar 

  22. Webster, C., Ivanov, S.H.: Robotics, artificial intelligence, and the evolving nature of work. In: George, B., Paul, J. (eds.) Business Transformation in Data Driven Societies. Palgrave-MacMillan, London (2019, Forthcoming)

    Google Scholar 

  23. Prihatno, A.T., Nurcahyanto, H., Jang, Y.M.: Artificial intelligence platform based for smart factory. In: 1st Korea Artificial Intelligence Conference (2020)

    Google Scholar 

  24. Artificial intelligence in industry: intelligent production. https://new.siemens.com/global/en/company/stories/industry/ai-in-industries.html

  25. O’Reilly, C., Binns, A.J.M.: The three stages of disruptive innovation: idea generation, incubation, and scaling. Calif. Manag. Rev. 61, 49–71 (2019)

    Google Scholar 

  26. Ehiorobo, O.A.: Strategic agility and ai-enabled resource capabilities for business survival in post-covid-19 global economy. Int. J. Inf. Bus. Manag. 12, 201–213 (2020)

    Google Scholar 

  27. Klumpp, M.: Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements. Int. J. Logist. Res. Appl. 21(3), 224–242 (2018)

    Google Scholar 

  28. Abdul-Rahman, et al.: [16] the author s an “Internet of things application using tethered msp430 to thinkspeak cloud” for the constant monitoring of the data and the information’s gathered in the real time

    Google Scholar 

  29. Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484 (2016)

    Google Scholar 

  30. Dorigo, M., Birattari, M.: Ant Colony Optimization. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8_22

    Book  Google Scholar 

  31. Malmborg, C.: A genetic algorithm for service level based vehicle scheduling. Eur. J. Oper. Res. 93, 121–134 (1996)

    Article  MATH  Google Scholar 

  32. Park, Y.B.: A hybrid genetic algorithm for the vehicle scheduling problem with due times and time deadlines. Int. J. Prod. Econ. 73(2), 175–188 (2001)

    Article  Google Scholar 

  33. Christo des, N.: Vehicle routing. In: Lawler, E., Lenstra, J., Rin, A., Kannooy, S.D. (eds.) The Traveling Salesman Problem, pp. 431–448. Wiley, Hoboken (1964)

    Google Scholar 

  34. Gesing, B., Peterson, S.J., Michelsen, D.: Artificial Intelligence In Logistics. A collaborative report by DHL and IBM on implications and use cases for the logistics industry (2018). https://www.logistics.dhl/content/dam/dhl/global/core/documents/pdf/glo-artificial-intelligence-in-logisticstrend-report.pdf. Accessed 14 Nov 2018

  35. Mortimer, G., Milford, M.: When AI meets your shopping experience it knows what you buy – and what you ought to buy. The Conversation, 31 August 2018

    Google Scholar 

  36. Metz, R.: Amazon’s cashier-less Seattle grocery store is opening to the public. MIT Technical review (2018). Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., et al.: Human level control through deep reinforcement learning. Nature 518(7540), 529–33 (2015)

    Google Scholar 

  37. Druehl, C., Carrillo, J., Hsuan, J.: Technological innovations: impacts on supply chains. In: Moreira, A., Ferreira, L., Zimmermann, R. (eds.) Innovation and Supply Chain Management. CMS, pp. 259–281. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74304-2_12

  38. Walker. J.: Machine Learning in Manufacturing: Present and Future Use Cases (2018). https://www.techemergence.com/machine-learning-in-manufacturing/. Accessed 13 Nov 2018

  39. Kaplan, A., Haenlein, M.: Business Horizons. Elsevier, Amsterdam (2019)

    Google Scholar 

  40. Moore, P.V.: OSH and the future of work: benefits and risks of artificial intelligence tools in workplaces. In: International Conference on Human-Computer Interaction (2019)

    Google Scholar 

  41. Mar, W., Thaw, Y.: An analysis of benefits and risks of artificial intelligence. Int. J. Trend Sci. Res. Dev. 3, 2456–6470 (2019)

    Google Scholar 

  42. Russell, S., Dewey, D., Tegmark, M.: Research priorities for robust and beneficial artificial intelligence. AI Mag. (2015). ojs.aaai.org

    Google Scholar 

  43. Baryannis, G., Dani, S., Validi, S., Antoniou, G.: Decision support systems and artificial intelligence in supply chain risk management. In: Zsidisin, G.A., Henke, M. (eds.) Revisiting Supply Chain Risk. SSSCM, vol. 7, pp. 53–71. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-03813-7_4

    Chapter  Google Scholar 

  44. Modgil, S., Singh, R.K., Hannibal, C.: Artificial intelligence for supply chain resilience: learning from COVID-19. Int. J. Logist. Manag. 33, 1246–1268 (2021). ISSN 0957-4093 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ENSAM School of Engineering, Meknes, Morocco

    Ikram Lebhar & Afaf Dadda

  2. University Moulay Ismail, Meknes, Morocco

    Latifa Ezzine

Authors
  1. Ikram Lebhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Afaf Dadda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Latifa Ezzine
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ikram Lebhar .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  2. Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lebhar, I., Dadda, A., Ezzine, L. (2023). Artificial Intelligence Applications in the Global Supply Chain: Benefits and Challenges. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 712. Springer, Cham. https://doi.org/10.1007/978-3-031-35251-5_27

Download citation

Publish with us

Policies and ethics

Search

Navigation