Resilient warehouse supply chains in post‑conflict economies: Evidence on prepositioning, multi‑echelon inventory, and lateral transshipment
Abstract
Purpose. This paper synthesizes empirical evidence on how to strengthen warehouse-centred supply chains in post-conflict economies facing damaged infrastructure, intermittent access, and volatile demand. Methodology. A systematic review of 15 protocol-verified empirical and simulation studies is conducted, coding contexts, warehouse interventions, and outcomes such as response time, service level/OTIF, inventory stability, delivery days, and unit logistics costs, with limited random-effects aggregation when comparable metrics are available. Results. Robust effects are identified for warehouse prepositioning, temporary or modular depots, lateral transshipment policies, and multi-echelon inventory control, which collectively reduce response times, increase service levels, and stabilise supply with moderate cost impacts. Digital enablers such as offline-capable warehouse management systems and energy‑autonomous facilities further enhance performance under grid and connectivity failures, though quantitative evidence remains sparse. Theoretical contribution. The review integrates the four-R flexibility perspective with the resilience capacities of absorption, adaptation, and recovery, showing how specific warehouse design and control levers operationalise resilience in the recovery phase of humanitarian and essential goods supply chains. Practical implications. The paper proposes a phased implementation roadmap for practitioners in post‑conflict settings, distinguishing quick wins in the first 0–3 months, network reconfiguration over 3–12 months, and longer‑term investments beyond 12 months to embed digital and energy autonomy in warehouse networks.
Sustainable Development Goals (SDGs): SDG 2: Zero Hunger; SDG 3: Good Health and Well‑Being; SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 16: Peace, Justice and Strong Institutions
Full text article
References
Anvari, M., Anvari, A., & Boyer, O. (2023). A prepositioning model for prioritized demand points considering lateral transshipment. Journal of Humanitarian Logistics and Supply Chain Management, 13(4), 433-455. https://doi.org/10.1108/JHLSCM-01-2023-0005
Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics, 11(2), 101-121. https://doi.org/10.1080/13675560701561789
Baskaya, S., Ertem, M. A., & Duran, S. (2017). Pre-positioning of relief items in humanitarian logistics considering lateral transhipment opportunities. Socio-Economic Planning Sciences, 57, 50-60. https://doi.org/10.1016/j.seps.2016.09.001
Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485-498. https://doi.org/10.1016/j.ijdrr.2017.01.017
Galindo, G., & Batta, R. (2013). Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies. Socio-Economic Planning Sciences, 47(1), 20-37. https://doi.org/10.1016/j.seps.2012.11.002
Khayal, D., Pradhananga, R., Pokharel, S., & Mutlu, F. (2015). A model for planning locations of temporary distribution facilities for emergency response. Socio-Economic Planning Sciences, 52, 22-30. https://doi.org/10.1016/j.seps.2015.09.002
Korucuk, S., Aytekin, A., Görçün, Ö., Simic, V., & Görçün, Ö. F. (2024). Warehouse site selection for humanitarian relief organizations using an interval-valued fermatean fuzzy LOPCOW-RAFSI model. Computers & Industrial Engineering, 192, 110160. https://doi.org/10.1016/j.cie.2024.110160
Lin, Y. H., Batta, R., Rogerson, P. A., Blatt, A., & Flanigan, M. (2012). Location of temporary depots to facilitate relief operations after an earthquake. Socio-Economic Planning Sciences, 46(2), 112-123. https://doi.org/10.1016/j.seps.2012.01.001
Rawls, C. G., & Turnquist, M. A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation research part B: Methodological, 44(4), 521-534. https://doi.org/10.1016/j.trb.2009.08.003
Roh, S. Y., Jang, H. M., & Han, C. H. (2013). Warehouse location decision factors in humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 29(1), 103-120. https://doi.org/10.1016/j.ajsl.2013.05.006
Roh, S., Pettit, S., Harris, I., & Beresford, A. (2015). The pre-positioning of warehouses at regional and local levels for a humanitarian relief organisation. International Journal of Production Economics, 170, 616-628. https://doi.org/10.1016/j.ijpe.2015.01.015
Roh, S. Y., Shin, Y. R., & Seo, Y. J. (2018). The Pre-positioned warehouse location selection for international humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 34(4), 297-307. https://doi.org/10.1016/j.ajsl.2018.12.003
Tavana, M., Abtahi, A. R., Di Caprio, D., Hashemi, R., & Yousefi-Zenouz, R. (2018). An integrated location-inventory-routing humanitarian supply chain network with pre-and post-disaster management considerations. Socio-Economic Planning Sciences, 64, 21-37. https://doi.org/10.1016/j.seps.2017.12.004
Zhang, X., & Chen, D. (2023). Prepositioning network design for humanitarian relief purposes under correlated demand uncertainty. Computers & Industrial Engineering, 182, 109365. https://doi.org/10.1016/j.cie.2023.109365
Authors
Copyright (c) 2025 Anatolii Nosar

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication, with the work simultaneously licensed under a CC BY 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.