IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04536092.html
   My bibliography  Save this paper

Comment l’Intelligence Artificielle dompte la traçabilité des processus Supply Chain ? Application à NOZ France

Author

Listed:
  • Hind Aboussikine

    (LIRSA - Laboratoire interdisciplinaire de recherche en sciences de l'action - CNAM - Conservatoire National des Arts et Métiers [CNAM] - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université)

  • Sonia Bendimerad

    (ESLI - École supérieure de logistique industrielle)

  • Thierry Sauvage

    (LIRSA - Laboratoire interdisciplinaire de recherche en sciences de l'action - CNAM - Conservatoire National des Arts et Métiers [CNAM] - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université)

  • Mohamed Haouari

    (ESLI - École supérieure de logistique industrielle)

Abstract

L'intelligence artificielle est devenue incontournable dans la mise en œuvre d'une supply chain efficace [Derrouiche, 2022] et les entreprises n'ont d'autres choix que de s'adapter pour bénéficier d'une logistique rentable, fiable et fluide. Dans ce contexte, la chaîne de magasin de déstockage généraliste-NOZ- souhaite renforcer sa présence en France en augmentant son nombre de magasins (de 322 à 450) et en améliorant la traçabilité de sa supply chain. Dans cette recherche, nous identifierons les différentes phases du projet pour mettre en place un système de traçabilité basée sur une application prédictive d'Intelligence Artificielle, puis, nous constaterons les retombées positives liées à ces actions et leurs impacts sur la stratégie logistique de Noz France.

Suggested Citation

  • Hind Aboussikine & Sonia Bendimerad & Thierry Sauvage & Mohamed Haouari, 2023. "Comment l’Intelligence Artificielle dompte la traçabilité des processus Supply Chain ? Application à NOZ France," Post-Print hal-04536092, HAL.
  • Handle: RePEc:hal:journl:hal-04536092
    Note: View the original document on HAL open archive server: https://cnam.hal.science/hal-04536092
    as

    Download full text from publisher

    File URL: https://cnam.hal.science/hal-04536092/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    2. Fran Casino & Venetis Kanakaris & Thomas K. Dasaklis & Socrates Moschuris & Spiros Stachtiaris & Maria Pagoni & Nikolaos P. Rachaniotis, 2021. "Blockchain-based food supply chain traceability: a case study in the dairy sector," International Journal of Production Research, Taylor & Francis Journals, vol. 59(19), pages 5758-5770, October.
    3. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    4. Amine Belhadi & Sachin Kamble & Samuel Fosso Wamba & Maciel M. Queiroz, 2022. "Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework," International Journal of Production Research, Taylor & Francis Journals, vol. 60(14), pages 4487-4507, July.
    5. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    Full references (including those not matched with items on IDEAS)

    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. Gupta, Shivam & Modgil, Sachin & Choi, Tsan-Ming & Kumar, Ajay & Antony, Jiju, 2023. "Influences of artificial intelligence and blockchain technology on financial resilience of supply chains," International Journal of Production Economics, Elsevier, vol. 261(C).
    2. Anna Trunk & Hendrik Birkel & Evi Hartmann, 2020. "On the current state of combining human and artificial intelligence for strategic organizational decision making," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 875-919, November.
    3. Mengmeng Wang & Xiaoming Pan, 2022. "Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance: An Empirical Analysis on an Emerging Market," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    4. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    5. Naoum Tsolakis & Roman Schumacher & Manoj Dora & Mukesh Kumar, 2023. "Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?," Annals of Operations Research, Springer, vol. 327(1), pages 157-210, August.
    6. Kinkel, Steffen & Capestro, Mauro & Di Maria, Eleonora & Bettiol, Marco, 2023. "Artificial intelligence and relocation of production activities: An empirical cross-national study," International Journal of Production Economics, Elsevier, vol. 261(C).
    7. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2021. "Sustainable Supply Chains with Blockchain, IoT and RFID: A Simulation on Order Management," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    8. Ulpan Tokkozhina & Ana Lucia Martins & Joao C. Ferreira, 2023. "Multi-tier supply chain behavior with blockchain technology: evidence from a frozen fish supply chain," Operations Management Research, Springer, vol. 16(3), pages 1562-1576, September.
    9. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    10. Shanyu Lin & Esra Sipahi Döngül & Serdar Vural Uygun & Mutlu Başaran Öztürk & Dinh Tran Ngoc Huy & Pham Van Tuan, 2022. "Exploring the Relationship between Abusive Management, Self-Efficacy and Organizational Performance in the Context of Human–Machine Interaction Technology and Artificial Intelligence with the Effect o," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    11. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    12. Giuseppe Varavallo & Giuseppe Caragnano & Fabrizio Bertone & Luca Vernetti-Prot & Olivier Terzo, 2022. "Traceability Platform Based on Green Blockchain: An Application Case Study in Dairy Supply Chain," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    13. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    14. Yang Shen, 2024. "Future jobs: analyzing the impact of artificial intelligence on employment and its mechanisms," Economic Change and Restructuring, Springer, vol. 57(2), pages 1-33, April.
    15. Chen, Pengyu & Chu, Zhongzhu & Zhao, Miao, 2024. "The Road to corporate sustainability: The importance of artificial intelligence," Technology in Society, Elsevier, vol. 76(C).
    16. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    17. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    18. Watson, Graeme J. & Desouza, Kevin C. & Ribiere, Vincent M. & Lindič, Jaka, 2021. "Will AI ever sit at the C-suite table? The future of senior leadership," Business Horizons, Elsevier, vol. 64(4), pages 465-474.
    19. Ivanov, Stanislav & Webster, Craig, 2024. "Automated decision-making: Hoteliers’ perceptions," Technology in Society, Elsevier, vol. 76(C).
    20. Samadhiya, Ashutosh & Yadav, Sanjeev & Kumar, Anil & Majumdar, Abhijit & Luthra, Sunil & Garza-Reyes, Jose Arturo & Upadhyay, Arvind, 2023. "The influence of artificial intelligence techniques on disruption management: Does supply chain dynamism matter?," Technology in Society, Elsevier, vol. 75(C).

    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:hal:journl:hal-04536092. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.