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A Multi-Agent Based System with Big Data Processing for Enhanced Supply Chain Agility

Author

Listed:
  • Mihalis Giannakis

    (Audencia Business School)

  • Michalis Louis

    (UCD - University College Dublin [Dublin])

Abstract

Purpose: Decision support systems have become an indispensable tool for managing complex supply chains. This paper develops a multi-agent based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored. Design/methodology/approach: For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled. Findings: Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility. Research limitations/implications: The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems. Practical implications: The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis. Originality/value. A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

Suggested Citation

  • Mihalis Giannakis & Michalis Louis, 2016. "A Multi-Agent Based System with Big Data Processing for Enhanced Supply Chain Agility," Post-Print hal-01353916, HAL.
    • Handle: RePEc:hal:journl:hal-01353916
    DOI: 10.1108/JEIM-06-2015-0050
    Note: View the original document on HAL open archive server: https://audencia.hal.science/hal-01353916
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    Citations

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    Cited by:

    1. Li, Xiang, 2020. "Reducing channel costs by investing in smart supply chain technologies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    2. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    3. Athar Ajaz Khan & János Abonyi, 2022. "Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy," Sustainability, MDPI, vol. 14(15), pages 1-40, August.
    4. Guy Burstein & Inon Zuckerman, 2023. "Deconstructing Risk Factors for Predicting Risk Assessment in Supply Chains Using Machine Learning," JRFM, MDPI, vol. 16(2), pages 1-16, February.
    5. May McMaster & Charlie Nettleton & Christeen Tom & Belanda Xu & Cheng Cao & Ping Qiao, 2020. "Risk Management: Rethinking Fashion Supply Chain Management for Multinational Corporations in Light of the COVID-19 Outbreak," JRFM, MDPI, vol. 13(8), pages 1-16, August.
    6. Ciampi, Francesco & Faraoni, Monica & Ballerini, Jacopo & Meli, Francesco, 2022. "The co-evolutionary relationship between digitalization and organizational agility: Ongoing debates, theoretical developments and future research perspectives," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    7. Wamba, Samuel Fosso & Dubey, Rameshwar & Gunasekaran, Angappa & Akter, Shahriar, 2020. "The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism," International Journal of Production Economics, Elsevier, vol. 222(C).
    8. Feldt, Julia & Kontny, Henning & Wagenitz, Axel, 2019. "Breaking through the bottlenecks using artificial intelligence," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg Int, volume 27, pages 30-56, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    9. Vaibhav S. Narwane & Rakesh D. Raut & Sachin Kumar Mangla & Manoj Dora & Balkrishna E. Narkhede, 2023. "Risks to Big Data Analytics and Blockchain Technology Adoption in Supply Chains," Annals of Operations Research, Springer, vol. 327(1), pages 339-374, August.
    10. Chih-Hung Hsu & Xu He & Ting-Yi Zhang & An-Yuan Chang & Wan-Ling Liu & Zhi-Qiang Lin, 2022. "Enhancing Supply Chain Agility with Industry 4.0 Enablers to Mitigate Ripple Effects Based on Integrated QFD-MCDM: An Empirical Study of New Energy Materials Manufacturers," Mathematics, MDPI, vol. 10(10), pages 1-35, May.
    11. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    12. v. Alberti-Alhtaybat, Larissa & Al-Htaybat, Khaldoon & Hutaibat, Khalid, 2019. "A knowledge management and sharing business model for dealing with disruption: The case of Aramex," Journal of Business Research, Elsevier, vol. 94(C), pages 400-407.
    13. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    14. Xu, Liming & Mak, Stephen & Brintrup, Alexandra, 2021. "Will bots take over the supply chain? Revisiting agent-based supply chain automation," International Journal of Production Economics, Elsevier, vol. 241(C).

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