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Growing Science » Uncertain Supply Chain Management » Digital twin applications in supply chain management: A systematic literature review

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Uncertain Supply Chain Management

ISSN 2291-6830 (Online) - ISSN 2291-6822 (Print)
Quarterly Publication
Volume 14 Issue 2 pp. 147-166 , 2026

Digital twin applications in supply chain management: A systematic literature review Pages 147-166 Right click to download the paper Download PDF

Authors: Sara Bouraya, Akram El Korchi

DOI: 10.5267/j.uscm.2025.2.001

Keywords: Digital twins Supply chain, Logistics, Simulation, Optimization, IoT, Artificial intelligence

Abstract: The new economic context has brought new challenges to the supply chain and has increased the complexity of its processes. The digitalization; as one of these challenges, is a rapidly evolving paradigm that transforms supply chains by integrating data and communication technologies to optimize operations, enhance sustainability, and improve overall performance. Digital twin technology emerged as one of the most promising digital tools that offer an innovative approach to supply chain management. However, the adoption of digital twins in the supply chain is still in its early stages. Previous research papers presented limited overviews of the applications of digital twin technology in supply chain systems that need to be extended, as it is inevitably a work in progress. In this matter, we conducted a systematic literature review built upon 31 articles to determine the applications of supply chain digital twins (SCDT). This study is divided into three core themes; the first is a comprehensive review of the paradigm of digital supply chain with a focus on digital twin technology and its primary features. The second theme presents an analysis of the 31 papers where we explore the different purposes of SCDTs and their integration. in the third theme by using VOSviewer to conduct a network analysis. We aim; through this paper, to contribute significantly to the supply chain management field by summarizing and analyzing existing research and developments in the applications of digital twins in the different areas of supply chains.

How to cite this paper
Bouraya, S & Korchi, A. (2026). Digital twin applications in supply chain management: A systematic literature review.Uncertain Supply Chain Management, 14(2), 147-166.

Refrences
Abideen, A. Z., Sundram, V. P. K., Pyeman, J., Othman, A. K., & Sorooshian, S. (2021). Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics. In Logistics (Vol. 5, Issue 4). MDPI. https://doi.org/10.3390/logistics5040084
Ashraf, M., Eltawil, A., & Ali, I. (2022). Time-To-Recovery Prediction in a Disrupted Three-Echelon Supply Chain Using LSTM. IFAC-PapersOnLine, 55(10), 1319–1324. https://doi.org/10.1016/j.ifacol.2022.09.573
Azevedo, S. G., Pimentel, C. M. O., Alves, A. C., & Matias, J. C. O. (2021). Support of advanced technologies in supply chain processes and sustainability impact. Applied Sciences (Switzerland), 11(7). https://doi.org/10.3390/app11073026
Badakhshan, E., & Ball, P. (2022). Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions. International Journal of Production Research. https://doi.org/10.1080/00207543.2022.2093682
Badakhshan, E., Ball, P., & Badakhshan, A. (2022). Using digital twins for inventory and cash management in supply chains. IFAC-PapersOnLine, 55(10), 1980–1985. https://doi.org/10.1016/j.ifacol.2022.09.689
Badia-Melis, R., Mc Carthy, U., Ruiz-Garcia, L., Garcia-Hierro, J., & Robla Villalba, J. I. (2018). New trends in cold chain monitoring applications - A review. In Food Control (Vol. 86, pp. 170–182). Elsevier Ltd. https://doi.org/10.1016/j.foodcont.2017.11.022
Barykin, S. Y., Bochkarev, A. A., Kalinina, O. V., & Yadykin, V. K. (2020). Concept for a supply chain digital twin. International Journal of Mathematical, Engineering and Management Sciences, 5(6), 1498–1515. https://doi.org/10.33889/IJMEMS.2020.5.6.111
Bhandal, R., Meriton, R., Kavanagh, R. E., & Brown, A. (2022). The application of digital twin technology in operations and supply chain management: a bibliometric review. In Supply Chain Management (Vol. 27, Issue 2, pp. 182–206). Emerald Group Holdings Ltd. https://doi.org/10.1108/SCM-01-2021-0053
Binsfeld, T., & Gerlach, B. (2022). Quantifying the Benefits of Digital Supply Chain Twins—A Simulation Study in Organic Food Supply Chains. Logistics, 6(3), 46. https://doi.org/10.3390/logistics6030046
Burgos, D., & Ivanov, D. (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, 152. https://doi.org/10.1016/j.tre.2021.102412
Busse, A., Gerlach, B., Lengeling, J. C., Poschmann, P., Werner, J., & Zarnitz, S. (2021). Towards Digital Twins of Multimodal Supply Chains. Logistics, 5(2). https://doi.org/10.3390/logistics5020025
Büyüközkan, G., & Göçer, F. (2018). Digital Supply Chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177. https://doi.org/10.1016/j.compind.2018.02.010
Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004
Cook, D. J., Mulrow, C. D., Haynes, R. B., & Mcmaster, F. (1997). Systematic Review Series Series Editors: Cynthia Mulrow f MD, MSc Deborah Cook f MD, MSc Systematic Reviews: Synthesis of Best Evidence for Clinical Decisions. In Ann Intern Med (Vol. 126). http://annals.org/
Defraeye, T., Tagliavini, G., Wu, W., Prawiranto, K., Schudel, S., Assefa Kerisima, M., Verboven, P., & Bühlmann, A. (2019). Digital twins probe into food cooling and biochemical quality changes for reducing losses in refrigerated supply chains. Resources, Conservation and Recycling, 149, 778–794. https://doi.org/10.1016/j.resconrec.2019.06.002
Elbouzidi, A. D., Bélanger, M. J., el Cadi, A. A., Pellerin, R., Lamouri, S., & Valencia, E. T. (2022). The Role Of AI In Warehouse Digital Twins. European Modeling and Simulation Symposium, EMSS. https://doi.org/10.46354/i3m.2022.emss.024
Farahani, P. (2015). Digital Supply Chain Management 2020 Vision. https://www.researchgate.net/publication/301350882
Feng, Y. (2018). Create the Individualized Digital Twin for Noninvasive Precise Pulmonary Healthcare. Significances of Bioengineering & Biosciences, 1(2). https://doi.org/10.31031/sbb.2018.01.000507
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
Gallego-García, S., Reschke, J., & García-García, M. (2019). Design and simulation of a capacity management model using a digital twin approach based on the viable system model: Case study of an automotive plant. Applied Sciences (Switzerland), 9(24). https://doi.org/10.3390/app9245567
Gerlach, B., Zarnitz, S., Nitsche, B., & Straube, F. (2021). Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits. Logistics, 5(4). https://doi.org/10.3390/logistics5040086
Gimpel, H., & Röglinger, M. (n.d.). Digital Transformation: Changes and Chances – Insights based on an Empirical Study. www.fim-rc.de
Henrichs, E., Noack, T., Piedrahita, A. M. P., Salem, M. A., Stolz, J., & Krupitzer, C. (2022). Can a byte improve our bite? An analysis of digital twins in the food industry. Sensors, 22(1). https://doi.org/10.3390/s22010115
Hofmann, W., & Branding, F. (2019). Implementation of an IoT- And cloud-based digital twin for real-time decision support in port operations. IFAC-PapersOnLine, 52(13), 2104–2109. https://doi.org/10.1016/j.ifacol.2019.11.516
IEEE Technology Engineering and Management Society., & Institute of Electrical and Electronics Engineers. (n.d.). Proceedings, 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) : virtual conference, 15 -17 June 2020.
Ivanov, D. (2018). Structural Dynamics and Resilience in Supply Chain Risk Management (Vol. 265). Springer International Publishing. https://doi.org/10.1007/978-3-319-69305-7
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136. https://doi.org/10.1016/j.tre.2020.101922
Ivanov, D. (2022). Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04754-9
Ivanov, D., & Dolgui, A. (2019). New disruption risk management perspectives in supply chains: Digital twins, the ripple effect, and resileanness. IFAC-PapersOnLine, 52(13), 337–342. https://doi.org/10.1016/j.ifacol.2019.11.138
Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning and Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450
Ivanov, D., & Dolgui, A. (2022). Stress testing supply chains and creating viable ecosystems. Operations Management Research, 15(1–2), 475–486. https://doi.org/10.1007/s12063-021-00194-z
Javaid, M., Haleem, A., & Suman, R. (2023). Digital Twin applications toward Industry 4.0: A Review. In Cognitive Robotics (Vol. 3, pp. 71–92). KeAi Communications Co. https://doi.org/10.1016/j.cogr.2023.04.003
Kaewunruen, S., Rungskunroch, P., & Welsh, J. (2019). A digital-twin evaluation of Net Zero Energy Building for existing buildings. Sustainability (Switzerland), 11(1). https://doi.org/10.3390/su11010159
Kamble, S. S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., & Sharma, R. (2022). Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework. Technological Forecasting and Social Change, 176. https://doi.org/10.1016/j.techfore.2021.121448
Kinnet, J. Creating a Digital Supply Chain: Monsanto’s Journey, SlideShare, 1–16. Volume 21. Available online: https://www. slideshare.net/BCTIM/creating-a-digital-supply-chain-monsantos-journey (accessed on 21 June 2015). 4.
Kirby, A. (2023). Exploratory Bibliometrics: Using VOSviewer as a Preliminary Research Tool. Publications, 11(1). https://doi.org/10.3390/publications11010010
Klar, R., Fredriksson, A., & Angelakis, V. (2023). Digital Twins for Ports: Derived from Smart City and Supply Chain Twinning Experience. http://arxiv.org/abs/2301.10224
Ko, C. S., Lee, H., & Kim, T. (2022). CONCEPTUAL MODELING FOR SUPPLY CHAIN DIGITAL TWIN. ICIC Express Letters, Part B: Applications, 13(5), 495–501. https://doi.org/10.24507/icicelb.13.05.495
Kosacka-Olejnik, M., Kostrzewski, M., Marczewska, M., Mrówczyńska, B., & Pawlewski, P. (2021). How digital twin concept supports internal transport systems?—Literature review. In Energies (Vol. 14, Issue 16). MDPI AG. https://doi.org/10.3390/en14164919
Koutsos, T. M., Menexes, G. C., & Dordas, C. A. (2019). An efficient framework for conducting systematic literature reviews in agricultural sciences. In Science of the Total Environment (Vol. 682, pp. 106–117). Elsevier B.V. https://doi.org/10.1016/j.scitotenv.2019.04.354
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
Lee, D., & Lee, S. (2021). Digital twin for supply chain coordination in modular construction. Applied Sciences (Switzerland), 11(13). https://doi.org/10.3390/app11135909
Leung, E. K. H., Lee, C. K. H., & Ouyang, Z. (2022). From traditional warehouses to Physical Internet hubs: A digital twin-based inbound synchronization framework for PI-order management. International Journal of Production Economics, 244. https://doi.org/10.1016/j.ijpe.2021.108353
Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model-based systems engineering. Systems, 7(1). https://doi.org/10.3390/systems7010007
Maheshwari, P., & Kamble, S. (2022a). The Application of Supply Chain Digital Twin to Measure Optimal Inventory Policy. IFAC-PapersOnLine, 55(10), 2324–2329. https://doi.org/10.1016/j.ifacol.2022.10.055
Maheshwari, P., & Kamble, S. (2022b). The Application of Supply Chain Digital Twin to Measure Optimal Inventory Policy. IFAC-PapersOnLine, 55(10), 2324–2329. https://doi.org/10.1016/j.ifacol.2022.10.055
Marmolejo-Saucedo, J. A. (2020). Design and Development of Digital Twins: a Case Study in Supply Chains. Mobile Networks and Applications, 25(6), 2141–2160. https://doi.org/10.1007/s11036-020-01557-9
Melesse, T. Y., Bollo, M., Pasquale, V. Di, Centro, F., & Riemma, S. (2022). Machine Learning-Based Digital Twin for Monitoring Fruit Quality Evolution. Procedia Computer Science, 200, 13–20. https://doi.org/10.1016/j.procs.2022.01.200
.Michael Grieves. (2015). Digital Twin: Manufacturing Excellence through Virtual Factory Replication DFAM-Design for Additive Manufacturing and Additive Manufacturing evaluation View project Organization, Operation, and Information Systems View project Michael Grieves Digital Twin Institute Digital Twin: Manufacturing Excellence through Virtual Factory Replication. https://www.researchgate.net/publication/275211047
Moder, P., Ehm, H., & Jofer, E. (2020). A Holistic Digital Twin Based on Semantic Web Technologies to Accelerate Digitalization. Lecture Notes in Electrical Engineering, 670 LNEE, 3–13. https://doi.org/10.1007/978-3-030-48602-0_1
Moshood, T. D., Nawanir, G., Sorooshian, S., & Okfalisa, O. (2021). Digital twins driven supply chain visibility within logistics: A new paradigm for future logistics. In Applied System Innovation (Vol. 4, Issue 2). MDPI AG. https://doi.org/10.3390/asi4020029
Nguyen, T., Duong, Q. H., Nguyen, T. Van, Zhu, Y., & Zhou, L. (2022). Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review. International Journal of Production Economics, 244. https://doi.org/10.1016/j.ijpe.2021.108381
Onwude, D. I., Chen, G., Eke-Emezie, N., Kabutey, A., Khaled, A. Y., & Sturm, B. (2020). Recent advances in reducing food losses in the supply chain of fresh agricultural produce. In Processes (Vol. 8, Issue 11, pp. 1–31). MDPI AG. https://doi.org/10.3390/pr8111431
Park, K. T., Son, Y. H., & Noh, S. Do. (2020). The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control. International Journal of Production Research, 1–22. https://doi.org/10.1080/00207543.2020.1788738
Perez, H. D., Wassick, J. M., & Grossmann, I. E. (2022). A digital twin framework for online optimization of supply chain business processes. Computers and Chemical Engineering, 166. https://doi.org/10.1016/j.compchemeng.2022.107972
Pilati, F., Tronconi, R., Nollo, G., Heragu, S. S., & Zerzer, F. (2021). Digital twin of covid‐19 mass vaccination centers. Sustainability (Switzerland), 13(13). https://doi.org/10.3390/su13137396
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58, 3–21. https://doi.org/10.1016/j.jmsy.2019.10.001
Qi, Q., Tao, F., Zuo, Y., & Zhao, D. (2018). Digital Twin Service towards Smart Manufacturing. Procedia CIRP, 72, 237–242. https://doi.org/10.1016/j.procir.2018.03.103
Raba, D., Tordecilla, R. D., Copado, P., Juan, A. A., & Mount, D. (2022). A Digital Twin for Decision Making on Livestock Feeding. Interfaces, 52(3), 267–282. https://doi.org/10.1287/inte.2021.1110
Santos, J. A. M., Lopes, M. R., Viegas, J. L., Vieira, S. M., & Sousa, J. M. C. (2020). Internal supply chain digital twin of a pharmaceutical company. IFAC-PapersOnLine, 53, 10797–10802. https://doi.org/10.1016/j.ifacol.2020.12.2864
Schluse, M., Atorf, L., & Rossmann, J. (2017). Experimentable Digital Twins for Model-Based Systems Engineering and Simulation-Based Development. IEEE.
Seif, A., Toro, C., & Akhtar, H. (2019). Implementing industry 4.0 asset administrative shells in mini factories. Procedia Computer Science, 159, 495–504. https://doi.org/10.1016/j.procs.2019.09.204
Shoji, K., Schudel, S., Shrivastava, C., Onwude, D., & Defraeye, T. (2022). Optimizing the postharvest supply chain of imported fresh produce with physics-based digital twins. Journal of Food Engineering, 329. https://doi.org/10.1016/j.jfoodeng.2022.111077
Singh Srai, J., Settanni, E., Tsolakis, N., & Kaur Aulakh, P. (2019). Supply Chain Digital Twins: Opportunities and Challenges Beyond the Hype TIGR2ESS (Transforming India’s Green Revolution by Research and Empowerment for Sustainable food Supplies) View project Availability-based modelling in the context of Product Service Systems-with applications to defence avionics View project Supply Chain Digital Twins: Opportunities and Challenges Beyond the Hype. 26–27. https://doi.org/10.17863/CAM.45897
Sinkovics, N. (2016). Enhancing the foundations for theorising through bibliometric mapping. International Marketing Review, 33(3), 327–350. https://doi.org/10.1108/IMR-10-2014-0341
Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018). Digital twin driven prognostics and health management for complex equipment. CIRP Annals, 67(1), 169–172. https://doi.org/10.1016/j.cirp.2018.04.055
Tozanli, özden, Kongar, E., & Gupta, S. M. (2020). Evaluation of waste electronic product trade-in strategies in predictive twin disassembly systems in the era of blockchain. Sustainability (Switzerland), 12(13). https://doi.org/10.3390/su12135416
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review *.
Uman, L. S. (2011). INFORMATION MANAGEMENT FOR THE BUSY PRACTITIONER Systematic Reviews and Meta-Analyses Information Management for the Busy Practitioner. In J Can Acad Child Adolesc Psychiatry (Vol. 20, Issue 1). www.cochrane.org
Van Der Valk, H., Strobel, G., Winkelmann, S., Hunker, J., & Tomczyk, M. (2022). Supply Chains in the Era of Digital Twins - A Review. Procedia Computer Science, 204, 156–163. https://doi.org/10.1016/j.procs.2022.08.019
Wang, K., Hu, Q., Zhou, M., Zun, Z., & Qian, X. (2021). Multi-aspect applications and development challenges of digital twin-driven management in global smart ports. Case Studies on Transport Policy, 9(3), 1298–1312. https://doi.org/10.1016/j.cstp.2021.06.014
Wang, L., Deng, T., Shen, Z. J. M., Hu, H., & Qi, Y. (2022). Digital twin-driven smart supply chain. In Frontiers of Engineering Management (Vol. 9, Issue 1, pp. 56–70). Higher Education Press Limited Company. https://doi.org/10.1007/s42524-021-0186-9
Wang, Y., Wang, X., & Liu, A. (2020). Digital twin-driven supply chain planning. Procedia CIRP, 93, 198–203. https://doi.org/10.1016/j.procir.2020.04.154
Wu, L., Yue, X., Jin, A., & Yen, D. C. (2016). Smart supply chain management: A review and implications for future research. International Journal of Logistics Management, 27(2), 395–417. https://doi.org/10.1108/IJLM-02-2014-0035
Zafarzadeh, M., Wiktorsson, M., & Baalsrud Hauge, J. (2021). A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective. In Logistics (Vol. 5, Issue 2). MDPI. https://doi.org/10.3390/logistics5020024
Zdolsek Draksler, T., Cimperman, M., & Obrecht, M. (2023). Data-Driven Supply Chain Operations—The Pilot Case of Postal Logistics and the Cross-Border Optimization Potential. Sensors, 23(3). https://doi.org/10.3390/s23031624
Zhang, G., MacCarthy, B. L., & Ivanov, D. (2022). The cloud, platforms, and digital twins—Enablers of the digital supply chain. In The Digital Supply Chain (pp. 77–91). Elsevier. https://doi.org/10.1016/B978-0-323-91614-1.00005-8
Zhang, J., Brintrup, A., Calinescu, A., Kosasih, E., & Sharma, A. (n.d.). Supply Chain Digital Twin Framework Design: An Approach of Supply Chain Operations Reference Model and System of Systems.
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Journal: Uncertain Supply Chain Management | Year: 2026 | Volume: 14 | Issue: 2 | Views: 1814 | Reviews: 0

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