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Analyzing the Implementation of Digital Twins in the Agri-Food Supply Chain

Author

Listed:
  • Tsega Y. Melesse

    (Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy)

  • Chiara Franciosi

    (Université de Lorraine, CNRS, CRAN UMR 7039, Campus Sciences, BP 70239, 54506 Vandeuvre-les-Nancy, France)

  • Valentina Di Pasquale

    (Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy)

  • Stefano Riemma

    (Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy)

Abstract
Background : Digital twins have the potential to significantly improve the efficiency and sustainability of the agri-food supply chain by providing visibility, reducing bottlenecks, planning for contingencies, and improving existing processes and resources. Additionally, they can add value to businesses by lowering costs and boosting customer satisfaction. This study is aimed at responding to common scientific questions on the application of digital twins in the agri-food supply chain, focusing on the benefits, types, integration levels, key elements, implementation steps, and challenges. Methods : This article conducts a systematic literature review of recent works on agri-food supply chain digital twins, using a list of peer-reviewed studies to analyze concepts using precise and well-defined criteria. Thus, 50 papers were selected based on inclusion and exclusion criteria, and descriptive and content-wise analysis was conducted to answer the research questions. Conclusions : The implementation of digital twins has shown promising advancements in addressing global challenges in the agri-food supply chain. Despite encouraging signs of progress in the sector, the real-world application of this solution is still in its early stages. This article intends to provide firms, experts, and researchers with insights into future research directions, implications, and challenges on the topic.

Suggested Citation

  • Tsega Y. Melesse & Chiara Franciosi & Valentina Di Pasquale & Stefano Riemma, 2023. "Analyzing the Implementation of Digital Twins in the Agri-Food Supply Chain," Logistics, MDPI, vol. 7(2), pages 1-17, June.
    • Handle: RePEc:gam:jlogis:v:7:y:2023:i:2:p:33-:d:1168879
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    References listed on IDEAS

    as
    1. Benno Gerlach & Simon Zarnitz & Benjamin Nitsche & Frank Straube, 2021. "Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits," Logistics, MDPI, vol. 5(4), pages 1-24, December.
    2. Mona Haji & Laoucine Kerbache & Mahaboob Muhammad & Tareq Al-Ansari, 2020. "Roles of Technology in Improving Perishable Food Supply Chains," Logistics, MDPI, vol. 4(4), pages 1-24, December.
    3. Kamble, Sachin S & Gunasekaran, Angappa & Parekh, Harsh & Mani, Venkatesh & Belhadi, Amine & Sharma, Rohit, 2022. "Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    4. Erma Suryani & Rully Agus Hendrawan & Isnaini Muhandhis & Rarasmaya Indraswari, 2022. "A simulation model to improve the value of rice supply chain (A case study in East Java – Indonesia)," Journal of Simulation, Taylor & Francis Journals, vol. 16(4), pages 392-414, July.
    5. Ahmed Zainul Abideen & Veera Pandiyan Kaliani Sundram & Jaafar Pyeman & Abdul Kadir Othman & Shahryar Sorooshian, 2021. "Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics," Logistics, MDPI, vol. 5(4), pages 1-22, November.
    6. Anuj Mittal & Caroline C. Krejci, 2019. "A hybrid simulation modeling framework for regional food hubs," Journal of Simulation, Taylor & Francis Journals, vol. 13(1), pages 28-43, January.
    7. Verdouw, Cor & Tekinerdogan, Bedir & Beulens, Adrie & Wolfert, Sjaak, 2021. "Digital twins in smart farming," Agricultural Systems, Elsevier, vol. 189(C).
    8. Long Wang & Lingli Li & Qingping Zhou & Lijun Pei, 2021. "Established Digital Model of Fruit Body Growth of Agrocybe cylindracea Based on Network Programming," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-9, July.
    9. Rahman, Md Mamunur & Nguyen, Ruby & Lu, Liang, 2022. "Multi-level impacts of climate change and supply disruption events on a potato supply chain: An agent-based modeling approach," Agricultural Systems, Elsevier, vol. 201(C).
    10. Anselm Busse & Benno Gerlach & Joel Cedric Lengeling & Peter Poschmann & Johannes Werner & Simon Zarnitz, 2021. "Towards Digital Twins of Multimodal Supply Chains," Logistics, MDPI, vol. 5(2), pages 1-12, April.
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    1. Adedotun Joseph Adenigbo & Joash Mageto & Rose Luke, 2023. "Adopting Technological Innovations in the Air Cargo Logistics Industry in South Africa," Logistics, MDPI, vol. 7(4), pages 1-16, November.

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