Supply and logistics impediments in natural disasters: an example from the 2023 Syria–Türkiye earthquakes

Purpose This study aims to evaluate the functionality of the emergency medical supply chain (EMSC) in northwest Syria (NWS) during the response to the February 6th, 2023 Türkiye–Syria earthquake, focusing on key challenges, strengths and areas for improvement in the system. Design/methodology/approach A mixed-methods approach was used, incorporating data from 15 health facilities through surveys and key informant interviews with supply chain personnel.. Findings This study uncovered significant vulnerabilities in the EMSC, particularly in procurement, forecasting, coordination, information systems and staffing. Emergency shipments were frequently delayed, with many facilities receiving incomplete supplies or lacking clarity on delivery timelines. Stockpiles played a critical role in the early response, but poor forecasting led to severe shortages where 92% of facilities lacked analgesics and 69% antibiotics. Cross-border disruptions further exposed the risk of overreliance on external suppliers. Communication was fragmented, with health responders relying on informal platforms like WhatsApp, underscoring the absence of robust, centralized communication tools during emergencies. Originality/value To the best of the authors’ knowledge, this study is the first to assess EMSC performance in NWS following the 2023 earthquake. It provides actionable insights into systemic weaknesses, emphasizing the urgent need for strategic reforms. Recommendations include establishing a national electronic logistics management information system, strengthening coordination protocols, enhancing prepositioning strategies and investing in structured training for supply chain personnel. With Syria transitioning toward unified governance, the findings highlight a pivotal opportunity to build a more resilient and responsive supply chain infrastructure, better equipped to manage future emergencies in conflict-affected and resource-constrained settings.