In today’s complex and unpredictable global environment, supply chain resilience (SCR) has become a primary focus for organisations attempting to navigate disruptions ranging from natural disasters and geopolitical tensions to pandemics and cyberattacks. The analysis of SCR is critical for maintaining business continuity and securing a competitive advantage, as it empowers firms to anticipate, absorb and recover from shocks that could otherwise lead to significant operational failure. While the concept of SCR is widely discussed, this study provides industry-specific depth by focusing on multimodal transportation networks – including aviation, maritime, rail and road sectors – and last-mile delivery operations. By utilising six real-world datasets, this research prepares the reader to understand how data-driven insights can be translated into practical resilience strategies across diverse logistical modules.

Resilience in supply chains involves more than just risk management; it requires a proactive approach to identifying vulnerabilities, adapting to changing conditions and rapidly restoring operations after unexpected events. This often includes strategies such as diversifying suppliers, increasing inventory buffers, enhancing collaboration with partners and investing in flexible logistics networks.

In recent years, data analysis has emerged as a powerful tool for strengthening SCR. By leveraging advanced analytics, machine learning and real-time data streams, organisations can gain deeper visibility into their supply chain operations, detect early warning signs of disruption and make more informed decisions. Data-driven insights enable companies to model potential risks, optimise resource allocation and develop contingency plans tailored to specific scenarios.

The integration of data analysis into supply chain management not only improves responsiveness but also supports continuous improvement. As organisations collect and analyse more data, they can identify patterns, predict future challenges and refine their resilience strategies over time. Ultimately, the combination of robust data analysis and resilient supply chain design empowers businesses to navigate uncertainty and sustain performance in the face of ongoing change.

Supply chain resilience has become a central focus in both academic research and industry practice, especially in the wake of global disruptions such as the coronavirus disease 2019 (COVID-19) pandemic. The following detailed review synthesises findings from 20 recent and influential studies, highlighting the evolution, mechanisms, technologies and challenges associated with building resilient supply chains.

Early research established SCR as the ability to anticipate, prepare for, respond to and recover from disruptions, emphasising flexibility, agility, redundancy and collaboration (Belhadi et al. 2021a; Kassa et al. 2023; Wu, Liu & Liang 2024; Zamani et al. 2022). Over time, the concept has expanded to include adaptability and transformation, with a growing emphasis on digitalisation and data-driven decision-making (Dubey et al. 2022; Gupta et al. 2023b; Ivanov 2023; Zamani et al. 2022).

Artificial intelligence (AI) and big data analytics (BDA) are widely recognised as transformative enablers of SCR. Systematic reviews show that AI and BDA support all phases of resilience, that is, readiness, response, recovery and adaptability by enabling real-time risk detection, demand forecasting, scenario simulation and agile decision-making (Beta, Nagaraj & Weerasinghe 2025; Gupta et al. 2023b; Ivanov 2023; Singh, Modgil & Shore 2023; Smyth et al. 2024; Zamani et al. 2022).

For example, Zamani et al. (2022) found that AI and BDA improve SCR by enhancing visibility, responsiveness and adaptability across supply chain phases.

Empirical studies demonstrate that AI-driven transparency, last-mile delivery and personalised solutions for stakeholders are essential for minimising disruption impacts and facilitating agile procurement (Dey et al. 2023; Modgil, Singh & Hannibal 2021b). Artificial intelligence-based multicriteria decision-making frameworks, such as those using fuzzy logic and machine learning, help organisations to identify and implement effective resilience strategies (Beta et al. 2025; Kassa et al. 2023).

Industry 4.0 technologies – including Internet of things (IoT), digital twins and blockchain – further enhance SCR by improving visibility, traceability and information sharing (Gupta et al. 2023b; Ivanov 2023; Riad, Naimi & Okar 2024). Deep learning and explainable AI have been applied to end-to-end resilience management, enabling accurate disruption risk forecasts and real-time mitigation strategies (Mahdi & Sadeghi 2025; Riad et al. 2024).

Digital intelligence technologies also empower sustainable and adaptive supply networks, as highlighted by Dubey et al. (2022). Leadership, organisational culture and employee competencies are essential for successful AI adoption and SCR, particularly in small and medium-sized enterprises (SMEs). Kassa et al. (2023) showed that leadership drives AI adoption, which in turn enhances agility, risk management and sustainable practices.

Human-centric approaches and internal mechanisms are essential for deriving value from digital transformation (Kassa et al. 2023; Shah, Gardas & Narwane 2021; Singh et al. 2024).

In humanitarian supply chains, AI-driven BDA is a significant determinant of agility, resilience, and performance, with organisational practices and information complexity playing key roles (Singh et al. 2023). The unique challenges of humanitarian contexts require tailored frameworks and a practice-based view, as traditional commercial models may not fully apply (Singh et al. 2023; Wu et al. 2024).

Despite the promise of AI and BDA, several challenges persist. These include data quality, integration complexity, cybersecurity, ethical concerns (such as job displacement and privacy) and the need for standardised resilience metrics (Belhadi et al. 2021a; Gupta et al. 2023a; Modgil et al. 2021a; Shah et al. 2021; Smyth et al. 2024). Reviews also highlight a lack of comprehensive frameworks for digital intelligence application and a need for more empirical studies, especially in emerging markets and under-researched sectors (Ahmed et al. 2023; Belhadi et al. 2021b; Dubey et al. 2022; Mahdi & Sadeghi 2025).

Recent literature calls for hybrid AI–human collaboration, greater transparency through explainable models and integration with other digital technologies (Dubey et al. 2022, Mahdi & Sadeghi 2025).

There is also a need for more research on the social and ethical implications of AI in SCR, as well as on the development of robust, scalable and context-specific resilience frameworks (Dubey et al. 2022; Gupta et al. 2023a; Mahdi & Sadeghi 2025; Modgil et al. 2021a; Shah et al. 2021).

Table 1 presents all the themes along with their key insights and corresponding references.

This section details the systematic investigation and the techniques used to interpret relationships among the study’s components. To ensure methodological rigour, this research employs a quantitative, data-driven strategy designed to move beyond a mere listing of variables and provide actionable insights for SCR. The methodology follows a three-step pipeline:

The process is illustrated in Figure 1.

This study addresses a critical gap in SCR: the lack of standardised, data-driven metrics to compare and monitor vulnerabilities across diverse transport modes. By analysing six real-world datasets, we have successfully met our two primary objectives: identifying systemic logistical vulnerabilities and providing a robust technical tool for operational monitoring.

Our findings provide a factual solution to the problem of identifying hidden risks. For instance, the discovery that 21% of flights experience disruptions and that 59% of rail incidents involve derailments highlights where infrastructure-specific resilience must be prioritised. Furthermore, the maritime data reveal that 60% of incidents stem from navigation errors, suggesting that human factors and real-time monitoring remain the most significant points of failure in that sector.

The proposed dimensionless metrics serve as the study’s core contribution, offering a scalable method for strategic management and crisis simulation. Instead of merely listing variables, this research demonstrates the interplay between environmental factors and operational success. The Amazon case study, in particular, proves that temporal and weather-related data are predictive of failure, with a 70% delay rate for orders placed after 17:00 and a 90% delay rate during heavy rain and traffic jams.

To address the logistical vulnerabilities identified in this study and fulfil the research purpose of enhancing SCR, the following exact actions are recommended for logistics managers, stakeholders and policymakers:

By implementing these actions, organisations can move beyond a reactive stance and achieve the proactive resilience necessary for maintaining business continuity in an unpredictable global environment.