The design of a model for the application of Fourth Industrial Revolution technologies in the humanitarian supply chain.

Introduction and Objectives: The rapid advancements associated with the Fourth Industrial Revolution—including the Internet of Things (IoT), artificial intelligence (AI), blockchain, big data analytics, robotics, and 3D printing—have created new opportunities to enhance efficiency, transparency, and responsiveness in humanitarian supply chains. However, the complex nature of relief operations, scarcity of resources, lack of digital infrastructure, and limited inter-organizational coordination have made the adoption of these technologies in crisis environments particularly challenging. Moreover, the literature indicates that most existing studies adopt isolated, technology-specific approaches, while comprehensive and integrated models explaining how Industry 4.0 technologies can be deployed in real crisis contexts remain limited. In this context, the present study aims to develop a conceptual model that systematically and contextually explains the influencing factors, challenges, implementation strategies, and potential outcomes of adopting Fourth Industrial Revolution technologies within humanitarian supply chains.
Methods: This study is applied in purpose and qualitative–exploratory in methodology, utilizing a grounded theory approach. Participants included eighteen experts comprising humanitarian logistics specialists, technology professionals, managers of government and non-governmental relief organizations, and crisis management officials. They were selected using purposive and snowball sampling. Data were collected through semi-structured interviews, fully transcribed, and analyzed using the three-stage coding process—open, axial, and selective coding—supported by MAXQDA software. Research validity was ensured through participant checking, independent coding by multiple researchers, and the application of credibility, transferability, dependability, and confirmability criteria. Theoretical saturation was achieved at the seventeenth interview.
Findings: Data analysis identified a set of causal conditions including the need for enhanced transparency, improved inter-organizational coordination, faster relief operations, and reduced human error. Contextual conditions such as weak communication infrastructure, unstable data networks, limited access to digital equipment, financial constraints, and the absence of shared standards among humanitarian organizations were also identified. Additionally, intervening factors such as cultural resistance, insufficient digital skills, cybersecurity threats, and the technical complexity of emerging technologies were found to significantly influence the implementation process.
The main strategies extracted from the data include developing technical infrastructures, creating modular and cloud-based platforms, strengthening inter-organizational collaboration, specialized staff training, establishing AI-based predictive systems, and deploying IoT, edge computing, robotics, and 3D printing technologies. The findings further revealed that these technologies not only operate independently but also function as components of an integrated “data cycle”: IoT generates data; cloud and edge computing process the data; AI analyzes it; and blockchain ensures its security and transparency. This cycle forms the technological backbone for operating effectively in high-uncertainty crisis environments.
The positive outcomes of successful technology adoption include improved supply chain resilience, reduced response time, enhanced resource traceability, reduced administrative corruption, and increased efficiency in resource allocation. However, potential negative consequences—such as over-reliance on technology, exposure to cyberattacks, and increased maintenance costs—were also identified.
Conclusion: The proposed conceptual model demonstrates that the effective implementation of Industry 4.0 technologies in humanitarian supply chains requires an integrated framework aligned with real-world crisis conditions. The model’s distinction between the preparedness phase (emphasizing prediction, planning, and infrastructure creation) and the response phase (emphasizing real-time monitoring, operational coordination, and live data analysis) enhances its practical applicability. This model can serve as a strategic guideline for policymakers, humanitarian organizations, and technology designers seeking to advance digital transformation within humanitarian operations.