IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v35y2024i8d10.1007_s10845-023-02253-7.html
   My bibliography  Save this article

A multi-agent based big data analytics system for viable supplier selection

Author

Listed:
  • Kamar Zekhnini

    (Moulay Ismail University, ENSAM)

  • Abla Chaouni Benabdellah

    (International University of Rabat)

  • Anass Cherrafi

    (EST-Safi, Cadi Ayyad University)

Abstract

The world is characterized by volatility, uncertainty, complexity, and ambiguity (VUCA). In such an environment, the viability in terms of digitalization, resilience, and sustainability capabilities has gained worldwide attention in supply chain management. Therefore, it is crucial to give special consideration to these paradigms when selecting suppliers. Moreover, the availability of data in digital supply chain systems can aid in supplier selection by using Artificial Intelligence techniques to identify viable suppliers. This approach can streamline the supplier selection process and lead to more efficient and effective manufacturing operations. Thus, it is necessary to have a big data analytics infrastructure in today’s data-driven world. In this context, this paper aims to design a multi-agent system that belongs to the theory of Distributed Artificial Intelligence based on big data analytics to give a strong tool for finding the best viable suppliers based on a thorough and data-driven evaluation. To do so, designing a multi-agent-based big data analytics system model necessitates identifying the multiple criteria needed for selecting viable suppliers in real-time decision-making. To this end, through a literature review, this paper analyzes more than 140 publications and identifies the main criteria needed for viable suppliers’ selection in the VUCA world. Therefore, the proposed system can be used as an intelligent viable supplier selection that improves the quality of the process and controls it while considering different capabilities. It presents a comprehensive model for viable supplier selection, consisting of four main layers: decision-making system, data resources, supplier selection, and big data analytics. The model incorporates six types of agents: Suppliers agent, Resource Agent, Knowledge Management Agent, Pilot Agent, Analyst Agent, and Decision-Making Agent. The integration of these layers and agents enables real-time data-driven decision-making, contributing to the selection of viable suppliers in a volatile and uncertain environment. The proposed model enhances supply chain performance in the digital era, offering a robust tool for both academics and practitioners to improve the quality of supplier selection.

Suggested Citation

  • Kamar Zekhnini & Abla Chaouni Benabdellah & Anass Cherrafi, 2024. "A multi-agent based big data analytics system for viable supplier selection," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3753-3773, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02253-7
    DOI: 10.1007/s10845-023-02253-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02253-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-023-02253-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Weber, Charles A. & Current, John R. & Benton, W. C., 1991. "Vendor selection criteria and methods," European Journal of Operational Research, Elsevier, vol. 50(1), pages 2-18, January.
    2. Dmitry Ivanov & Alexandre Dolgui, 2022. "Stress testing supply chains and creating viable ecosystems," Operations Management Research, Springer, vol. 15(1), pages 475-486, June.
    3. Dmitry Ivanov, 2021. "Supply Chain Viability and the COVID-19 pandemic: a conceptual and formal generalisation of four major adaptation strategies," International Journal of Production Research, Taylor & Francis Journals, vol. 59(12), pages 3535-3552, June.
    4. Kamar Zekhnini & Anass Cherrafi & Imane Bouhaddou & Youssef Benghabrit & Jose Arturo Garza-Reyes, 2020. "Supply chain management 4.0: a literature review and research framework," Post-Print hal-04334635, HAL.
    5. Kamar Zekhnini & Anass Cherrafi & Imane Bouhaddou & Abla Chaouni Benabdellah & Surajit Bag, 2021. "A model integrating lean and green practices for viable, sustainable, and digital supply chain performance," Post-Print hal-04334647, HAL.
    6. Amin Mahmoudi & Saad Ahmed Javed & Abbas Mardani, 2022. "Gresilient supplier selection through Fuzzy Ordinal Priority Approach: decision-making in post-COVID era," Operations Management Research, Springer, vol. 15(1), pages 208-232, June.
    7. Ghadimi, Pezhman & Ghassemi Toosi, Farshad & Heavey, Cathal, 2018. "A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain," European Journal of Operational Research, Elsevier, vol. 269(1), pages 286-301.
    8. Morteza Yazdani & Dragan Pamucar & Prasenjit Chatterjee & Ali Ebadi Torkayesh, 2022. "“A multi-tier sustainable food supplier selection model under uncertainty”," Operations Management Research, Springer, vol. 15(1), pages 116-145, June.
    9. Alexandre Dolgui & Dmitry Ivanov, 2022. "5G in digital supply chain and operations management: fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything," International Journal of Production Research, Taylor & Francis Journals, vol. 60(2), pages 442-451, January.
    10. Kamar Zekhnini & Anass Cherrafi & Imane Bouhaddou & Abla Chaouni Benabdellah & Rakesh Raut, 2021. "A holonic architecture for the supply chain performance in industry 4.0 context," Post-Print hal-04335016, HAL.
    11. Dmitry Ivanov, 2018. "Revealing interfaces of supply chain resilience and sustainability: a simulation study," International Journal of Production Research, Taylor & Francis Journals, vol. 56(10), pages 3507-3523, May.
    12. A. Azadeh & R. Siadatian & M. Rezaei-Malek & F. Rouhollah, 2017. "Optimization of supplier selection problem by combined customer trust and resilience engineering under uncertainty," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1553-1566, November.
    13. Sarah Castaldi & Miriam M. Wilhelm & Sjoerd Beugelsdijk & Taco Vaart, 2023. "Extending Social Sustainability to Suppliers: The Role of GVC Governance Strategies and Supplier Country Institutions," Journal of Business Ethics, Springer, vol. 183(1), pages 123-146, February.
    14. Sebastian Raisch & Julian Birkinshaw & Gilbert Probst & Michael L. Tushman, 2009. "Organizational Ambidexterity: Balancing Exploitation and Exploration for Sustained Performance," Organization Science, INFORMS, vol. 20(4), pages 685-695, August.
    15. Li, Saiwei & Lopez, Rigoberto A. & Liu, Yumei, 2020. "Consumer preferences for sustainably produced milk in China," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304193, Agricultural and Applied Economics Association.
    16. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    17. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Shore, Adam & Ram, Pratibha, 2023. "Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    18. Cavalcante, Ian M. & Frazzon, Enzo M. & Forcellini, Fernando A. & Ivanov, Dmitry, 2019. "A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing," International Journal of Information Management, Elsevier, vol. 49(C), pages 86-97.
    19. Bernardo Nicoletti, 2023. "Supply Network 5. Resilience and Agility," Springer Books, in: Supply Network 5.0, chapter 6, pages 191-226, Springer.
    20. Dmitry Ivanov & Ajay Das, 2020. "Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note," International Journal of Integrated Supply Management, Inderscience Enterprises Ltd, vol. 13(1), pages 90-102.
    21. Seyedmohsen Hosseini & Abdullah Al Khaled, 2019. "A hybrid ensemble and AHP approach for resilient supplier selection," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 207-228, January.
    22. Kamalaldin, Anmar & Sjödin, David & Hullova, Dusana & Parida, Vinit, 2021. "Configuring ecosystem strategies for digitally enabled process innovation: A framework for equipment suppliers in the process industries," Technovation, Elsevier, vol. 105(C).
    23. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    24. Christian F. Durach & Joakim Kembro & Andreas Wieland, 2017. "A New Paradigm for Systematic Literature Reviews in Supply Chain Management," Journal of Supply Chain Management, Institute for Supply Management, vol. 53(4), pages 67-85, October.
    25. Roodhooft, Filip & Konings, Jozef, 1997. "Vendor selection and evaluation an Activity Based Costing approach," European Journal of Operational Research, Elsevier, vol. 96(1), pages 97-102, January.
    26. Dmitry Ivanov & Alexandre Dolgui, 2020. "Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak," International Journal of Production Research, Taylor & Francis Journals, vol. 58(10), pages 2904-2915, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ekene Cynthia Onukwulu & Mercy Odochi-Agho & Nsisong Louis Eyo-Udo, 2024. "Innovations in Supplier Evaluation: Frameworks and Techniques for Supply Chain Resilience," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(12), pages 610-623, December.

    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. Muhammad Umar Farooq & Amjad Hussain & Tariq Masood & Muhammad Salman Habib, 2021. "Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19," Sustainability, MDPI, vol. 13(5), pages 1-33, February.
    2. Zhao, Nanyang & Hong, Jiangtao & Lau, Kwok Hung, 2023. "Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model," International Journal of Production Economics, Elsevier, vol. 259(C).
    3. Dmitry Ivanov & Alexandre Dolgui, 2022. "Stress testing supply chains and creating viable ecosystems," Operations Management Research, Springer, vol. 15(1), pages 475-486, June.
    4. Balezentis, Tomas & Zickiene, Agne & Volkov, Artiom & Streimikiene, Dalia & Morkunas, Mangirdas & Dabkiene, Vida & Ribasauskiene, Erika, 2023. "Measures for the viable agri-food supply chains: A multi-criteria approach," Journal of Business Research, Elsevier, vol. 155(PA).
    5. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    6. 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.
    7. Ghanei, Shima & Contreras, Ivan & Cordeau, Jean-François, 2023. "A two-stage stochastic collaborative intertwined supply network design problem under multiple disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    8. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    9. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    10. Rozhkov, Maxim & Ivanov, Dmitry & Blackhurst, Jennifer & Nair, Anand, 2022. "Adapting supply chain operations in anticipation of and during the COVID-19 pandemic," Omega, Elsevier, vol. 110(C).
    11. Chervenkova, Tanya & Ivanov, Dmitry, 2023. "Adaptation strategies for building supply chain viability: A case study analysis of the global automotive industry re-purposing during the COVID-19 pandemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    12. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    13. Ivanov, Dmitry, 2024. "Supply chain resilience: Conceptual and formal models drawing from immune system analogy," Omega, Elsevier, vol. 127(C).
    14. Seyedmohsen Hosseini & Dmitry Ivanov, 2022. "A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach," Annals of Operations Research, Springer, vol. 319(1), pages 581-607, December.
    15. Burgos, Diana & Ivanov, Dmitry, 2021. "Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    16. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    17. Islam, Samiul & Amin, Saman Hassanzadeh & Wardley, Leslie J., 2021. "Machine learning and optimization models for supplier selection and order allocation planning," International Journal of Production Economics, Elsevier, vol. 242(C).
    18. Aghajani, Mojtaba & Ali Torabi, S. & Altay, Nezih, 2023. "Resilient relief supply planning using an integrated procurement-warehousing model under supply disruption," Omega, Elsevier, vol. 118(C).
    19. Li, Guo & Xue, Jing & Li, Na & Ivanov, Dmitry, 2022. "Blockchain-supported business model design, supply chain resilience, and firm performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    20. Mohammadi, Mehrdad & Dehghan, Milad & Pirayesh, Amir & Dolgui, Alexandre, 2022. "Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic," Omega, Elsevier, vol. 113(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:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02253-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.