AI and big data boost crisis response efficiency, yet adoption remains uneven worldwide

Despite the global momentum behind big data-driven humanitarian logistics, the study warns that emerging economies face significant structural challenges. These include fragile infrastructure, limited digital literacy, inconsistent connectivity and weak coordination mechanisms across government and non-government stakeholders. In many developing regions, humanitarian actors operate with incomplete datasets, outdated reporting channels and low interoperability between emergency networks. These conditions directly impede the deployment of predictive analytics and coordinated disaster response.

Humanitarian operations around the world are undergoing a rapid technological shift as big data analytics, artificial intelligence and digital platforms become vital to disaster response. A new global analysis shows that while these tools are strengthening coordination and resilience in many regions, emerging economies still face major barriers to adopting data-driven humanitarian supply chains. The research points to widening digital divides that could directly affect future disaster preparedness, response speed and the survival outcomes of vulnerable populations.

The findings come from the study “A Systematic Analysis of Big Data-Driven Humanitarian Supply Chain Management Research: Implications for Emerging Economies,” published in Administrative Sciences, which examines global developments across 64 peer-reviewed studies in humanitarian logistics and digital transformation. The review maps how big data technologies are shifting the sector’s strategic priorities, theoretical foundations and practical applications.

The analysis shows a steep rise in academic and operational attention to data-centric humanitarian systems, particularly since 2018, as governments, NGOs and private-sector partners increasingly rely on machine-learning models, IoT networks, satellite feeds and blockchain platforms to address climate-driven emergencies and geopolitical instability.

Big data transforming humanitarian operations

The study finds that big data capabilities have moved from fringe experimentation to core operational infrastructure within humanitarian supply chain management. Across the literature, big data analytics consistently strengthen visibility, predictive accuracy, trust and coordination between relief agencies and government authorities. Research demonstrates that digital tools improve forecasting of disaster impacts, enabling faster mobilization of relief supplies and more accurate prioritization of high-risk zones.

Several influential studies highlight how big data platforms allow critical information to flow between civil and military agencies, humanitarian NGOs and local authorities. These systems integrate structured and unstructured data, including sensor readings, social media signals, drone imagery, field assessments and satellite observations. When combined, these inputs support adaptable routing, inventory control, demand forecasting and risk assessment under highly volatile disaster conditions.

Artificial intelligence models are increasingly used to optimize relief operations, identifying the most effective distribution paths, anticipating shortages and supporting decisions during flood response, drought management and health emergencies. As big data becomes embedded in humanitarian logistics, operational agility improves, decision cycles shorten and resource allocation becomes more precise.

The review identifies six dominant research themes shaping the field. Big data and humanitarian logistics emerge as the central focus, capturing the integration of IoT sensors, telematics and real-time analytics. Digital technologies, including AI and machine learning, represent a transition theme showing rapid growth and close alignment with the core humanitarian agenda. The humanitarian supply chain theme anchors the field conceptually, linking foundational logistics research with modern technological enablers. Additional themes include emergency logistics, blockchain for traceability and sustainability in humanitarian systems.

Together, these themes reflect a sector undergoing a structural transformation, where technology not only optimizes existing processes but redefines how humanitarian aid is anticipated, delivered and evaluated.

Emerging economies confront barriers that limit digital humanitarian readiness

Despite the global momentum behind big data-driven humanitarian logistics, the study warns that emerging economies face significant structural challenges. These include fragile infrastructure, limited digital literacy, inconsistent connectivity and weak coordination mechanisms across government and non-government stakeholders. In many developing regions, humanitarian actors operate with incomplete datasets, outdated reporting channels and low interoperability between emergency networks. These conditions directly impede the deployment of predictive analytics and coordinated disaster response.

The research shows that humanitarian efforts in emerging economies often struggle to establish centralized data repositories and integrated platforms capable of handling real-time information. Without such systems, relief agencies cannot effectively forecast supply needs, allocate resources or anticipate bottlenecks. The absence of standardized data protocols leaves agencies dependent on manual methods prone to delays and misjudgment.

Another challenge identified is the lack of trained personnel equipped to manage data pipelines, interpret analytical outputs or maintain digital infrastructure. Skilled data scientists and emergency analysts are scarce in many emerging markets, forcing agencies to rely heavily on international partners. This dependence slows response times and reduces the long-term sustainability of local humanitarian supply chain systems.

The review also highlights socioeconomic and governance constraints. Budget limitations prevent many governments from investing in advanced data systems. Contextual barriers such as political instability, weak institutions and fragmented emergency networks further complicate digital transformation. Even when technology is available, many affected regions lack the enabling ecosystem required for successful adoption.

Moreover, research collaboration in this field remains heavily concentrated in high-income countries. The mapping of institutional partnerships shows that developed nations dominate the knowledge landscape, while emerging economies contribute relatively few studies and face limited representation in global research networks. This gap restricts the development of context-specific solutions tailored to the unique operational environments of developing countries.

The study notes that although some emerging markets are experimenting with IoT-enabled logistics, drone-based assessments and blockchain verification systems, these efforts remain small-scale, isolated and dependent on external funding. Without strategic investment and policy alignment, digital inequities in humanitarian logistics may widen, placing vulnerable populations at greater risk during future disasters.

Strategic pathways for data-driven humanitarian readiness

The analysis provides critical direction for policymakers, humanitarian leaders and researchers seeking to strengthen resilience in developing regions. One of the central recommendations is the expansion of theoretical frameworks applied to humanitarian supply chains. The study argues that perspectives such as the resource-based view, dynamic capabilities theory and information processing theory should be redesigned for emerging-economy contexts, where resource constraints and uncertainty are significantly higher.

The review underscores the importance of building digital capacity within local humanitarian systems. Governments and aid organizations are encouraged to develop data-sharing networks, adopt interoperable platforms, train supply-chain personnel in analytics and invest in scalable information systems. These measures can help create the foundational infrastructure needed for effective big data adoption.

Equally important is improving collaboration across public, private and nonprofit actors. Multistakeholder partnerships can support the implementation of advanced technologies, transfer of expertise and alignment of data governance policies. Stronger coordination enhances institutional readiness and ensures timely mobilization during crises.

The study presents blockchain as a promising niche technology with potential to improve transparency, traceability and trust in humanitarian operations. By creating secure, tamper-proof records of transactions and supply movements, blockchain can help reduce fraud, strengthen procurement oversight and improve donor confidence. For emerging economies, where governance systems may be less robust, such technologies can play a vital role in reinforcing accountability.

Sustainability also appears as a key theme, as humanitarian systems increasingly integrate environmentally conscious strategies into logistics planning. Digital tools can support the optimization of routes, reduction of waste, improved energy usage and responsible sourcing. As climate-driven disasters intensify, sustainability and resilience become interlinked objectives that rely on strong data ecosystems.

The study calls for expanding empirical research in developing regions. Greater academic engagement can reveal context-specific vulnerabilities, generate new frameworks and support the development of affordable solutions suited to local constraints. Without localized evidence, emerging economies risk relying on models that may not reflect their operational realities.