Multimodal Logistics for Resilient and Sustainable Global Supply Chains: Strategic Insights from Integrated Transport Systems
DOI:
https://doi.org/10.61194/sijl.v2i4.731Keywords:
Multimodal Transportation, Integrated Logistics Networks, Synchromodal Logistics, Supply Chain Resilience, IoT in Logistics, Green Logistics, Transportation OptimizationAbstract
Multimodal transportation has emerged as a critical solution for enhancing the efficiency and resilience of integrated logistics networks. This narrative review aims to synthesize current research on the effectiveness of multimodal logistics in addressing global supply chain challenges. Using a structured keyword-based search across Scopus, Web of Science, and Google Scholar, this study selected peer-reviewed literature published between 2010 and 2024. Emphasis was placed on studies focusing on cost optimization, environmental impact, digital integration, and strategic network planning. The review identifies five key dimensions—logistics cost-efficiency, crisis resilience, environmental sustainability, technological integration, and network design—as central to the performance of multimodal systems. Optimization models significantly reduce logistics costs and emissions, while real-time data and IoT systems enhance operational coordination. In crisis contexts, multimodal approaches offer adaptable responses, particularly when supported by digital infrastructure. However, challenges such as regulatory fragmentation and infrastructure disparity limit broader implementation, especially in developing regions. Synchromodal strategies and policy alignment are highlighted as critical enablers of system responsiveness and sustainability. This review concludes that strategic technological and institutional reforms are essential to unlock the full potential of multimodal logistics. Future research should explore adaptive AI-driven models and conduct cross-regional analyses to inform context-specific solutions. Strengthening digital infrastructure and fostering stakeholder collaboration will be key in developing resilient and future-ready logistics systems.
References
Alam, T., Gupta, R., Ahamed, N., Ullah, A., & Almaghthwi, A. (2024). Smart mobility adoption in sustainable smart cities to establish a growing ecosystem: challenges and opportunities. Mrs Energy & Sustainability, 11(2), 304-316. https://doi.org/10.1557/s43581-024-00092-4 DOI: https://doi.org/10.1557/s43581-024-00092-4
Alves, P., Melo, I., Branco, J., Bartholomeu, D., & Filho, J. (2021). Which green transport corridors (GTC) are efficient? A dual-step approach using Network Equilibrium Model (NEM) and Data Envelopment Analysis (DEA). Journal of Marine Science and Engineering, 9(3), 247. https://doi.org/10.3390/jmse9030247 DOI: https://doi.org/10.3390/jmse9030247
Brochado, Â., Rocha, E., & Costa, D. (2024). A modular IoT-based architecture for logistics service performance assessment and real-time scheduling towards a synchromodal transport system. Sustainability, 16(2), 742. https://doi.org/10.3390/su16020742 DOI: https://doi.org/10.3390/su16020742
Chen, S., Yan, Y., & Song, H. (2010). Optimal logistics hubs locations on the multimodal transportation network., 2850-2855. https://doi.org/10.1061/41139(387)399 DOI: https://doi.org/10.1061/41139(387)399
Dérpich, I., Durán, C., Carrasco, R., Moreno, F., Fernández‐Campusano, C., & Espinosa-Leal, L. (2024). Pursuing optimization using multimodal transportation system: A strategic approach to minimizing costs and CO2 emissions. Journal of Marine Science and Engineering, 12(6), 976. https://doi.org/10.3390/jmse12060976 DOI: https://doi.org/10.3390/jmse12060976
Fernando, S., & Jha, P. (2021). Exploring the impacts of economic corridors on South Asian countries. India Quarterly: A Journal of International Affairs, 77(3), 404-423. https://doi.org/10.1177/09749284211027145 DOI: https://doi.org/10.1177/09749284211027145
Ghiara, H., & Ne’Tori, G. (2013). ICTs as a mighty resource for cutting edge cities: Case study – Genoa and its port. https://doi.org/10.2495/sc130862 DOI: https://doi.org/10.2495/SC130862
Heddebaut, O., & Ciommo, F. (2017). City-hubs for smarter cities: The case of Lille “EuraFlandres” interchange. European Transport Research Review, 10(1). https://doi.org/10.1007/s12544-017-0283-3 DOI: https://doi.org/10.1007/s12544-017-0283-3
Liu, H., Song, G., Liu, T., & Guo, B. (2022). Multitask emergency logistics planning under multimodal transportation. Mathematics, 10(19), 3624. https://doi.org/10.3390/math10193624 DOI: https://doi.org/10.3390/math10193624
Makarova, I., Serikkaliyeva, A., Gubacheva, L., Mukhametdinov, E., Buyvol, P., Barinov, A., … & Mavlyautdinova, G. (2023). The role of multimodal transportation in ensuring sustainable territorial development: Review of risks and prospects. Sustainability, 15(7), 6309. https://doi.org/10.3390/su15076309 DOI: https://doi.org/10.3390/su15076309
Okyere, S., Yang, J., Aning, K., & Zhan, B. (2019). Review of sustainable multimodal freight transportation system in African developing countries: Evidence from Ghana. International Journal of Engineering Research in Africa, 41, 155-174. https://doi.org/10.4028/www.scientific.net/jera.41.155 DOI: https://doi.org/10.4028/www.scientific.net/JERA.41.155
Pongsayaporn, P., & Chinda, T. (2022). Long-term strategies for multimodal transportation of block rubber in Thailand. Sustainability, 14(22), 15350. https://doi.org/10.3390/su142215350 DOI: https://doi.org/10.3390/su142215350
Rentschler, J., Elbert, R., & Weber, F. (2022). Promoting sustainability through synchromodal transportation: A systematic literature review and future fields of research. Sustainability, 14(20), 13269. https://doi.org/10.3390/su142013269 DOI: https://doi.org/10.3390/su142013269
Ri-Qing, L., Liu, W., & Yuan, Y. (2023). Resilience improvement and risk management of multimodal transport logistics in the post–COVID-19 era: The case of TIR-based sea–road multimodal transport logistics. Sustainability, 15(7), 6041. https://doi.org/10.3390/su15076041 DOI: https://doi.org/10.3390/su15076041
Şahan, D., & Tuna, O. (2021). Policy implications on transport infrastructure–trade dynamics: Case of Turkey. Logistics, 5(3), 47. https://doi.org/10.3390/logistics5030047 DOI: https://doi.org/10.3390/logistics5030047
Steenbergen, R., Brunetti, M., & Mes, M. (2021). Network generation for simulation of multimodal logistics systems., 1-12. https://doi.org/10.1109/wsc52266.2021.9715497 DOI: https://doi.org/10.1109/WSC52266.2021.9715497
Sun, Y., Lang, M., & Wang, D. (2015). Optimization models and solution algorithms for freight routing planning problem in the multi-modal transportation networks: A review of the state-of-the-art. The Open Civil Engineering Journal, 9(1), 714-723. https://doi.org/10.2174/1874149501509010714 DOI: https://doi.org/10.2174/1874149501509010714
Xu, Z., Zheng, C., Zheng, S., Ma, G., & Chen, Z. (2024). Multimodal transportation route optimization of emergency supplies under uncertain conditions. Sustainability, 16(24), 10905. https://doi.org/10.3390/su162410905 DOI: https://doi.org/10.3390/su162410905
Zhang, J., Li, H., Han, W., & Li, Y. (2024). Research on optimization of multimodal hub-and-spoke transport network under uncertain demand. Archives of Transport, 70(2), 137-157. https://doi.org/10.61089/aot2024.1g17bx18 DOI: https://doi.org/10.61089/aot2024.1g17bx18
Zhang, X., Jin, F., Yuan, X., & Zhang, H. (2021). Low-carbon multimodal transportation path optimization under dual uncertainty of demand and time. Sustainability, 13(15), 8180. https://doi.org/10.3390/su13158180 DOI: https://doi.org/10.3390/su13158180
Negesse, A., Woyraw, W., Temesgen, H., Teka, Y., Yismaw, L., Akalu, T., … & Desalegn, B. (2022). Spatial exploration of non-resilience to food insecurity, its association with COVID-19 and household coping strategies in East Gojjam districts, northwest Ethiopia, 2020. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-19963-2 DOI: https://doi.org/10.1038/s41598-022-19963-2
Song, M., Ma, X., Zhao, X., & Zhang, L. (2022). How to enhance supply chain resilience: A logistics approach. The International Journal of Logistics Management, 33(4), 1408-1436. https://doi.org/10.1108/ijlm-04-2021-0211 DOI: https://doi.org/10.1108/IJLM-04-2021-0211
Tang, Y., Chau, K., Lau, Y., & Zheng, Z. (2023). Data-intensive inventory forecasting with artificial intelligence models for cross-border e-commerce service automation. Applied Sciences, 13(5), 3051. https://doi.org/10.3390/app13053051 DOI: https://doi.org/10.3390/app13053051
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