Supply chain risk management has become increasingly crucial due to the complexity and interconnectivity of modern supply chains. Among various risk factors, cascading failures have emerged as a significant concern for risk management. This study systematically reviews the literature published between 2013 and 2024, focusing on cascading failures in supply chain networks. A total of 92 articles were selected from the Web of Science (WoS) database for in-depth analysis. The findings indicate that future research needs to achieve breakthroughs in three dimensions: data acquisition, theoretical innovation, and practical application. Specifically, integrating multi-source data is crucial for enhancing the accuracy, comprehensiveness, and timeliness of data in supply chain networks. Higher-order network modeling and digital twin technology will aid in more accurately simulating and predicting cascading failures. From a theoretical standpoint, exploring hybrid failure mechanisms and multi-level propagation patterns will deepen the understanding of failure propagation in complex networks. From a practical perspective, developing resilience design standards, intelligent early warning systems, and differentiated policy tools will strengthen the risk resistance capabilities of supply chains. Overcoming these challenges will require a holistic approach that combines data-driven insights, theoretical advancements, and practical solutions to build a safer and more resilient global supply chain system.

Purpose Managers may understand the concepts of collaboration and risk management as complementary. This study investigates whether this understanding is associated with mitigation actions within the supply chain in the post-COVID-19 context. The analysis focuses on the level of uncertainty related to managers' adaptive performance, and the impacts of the pandemic on the links between collaboration and risk management. The role of managers' perceived leadership effectiveness is also evaluated as a potential mediator. Design/methodology/approach A survey was conducted with 243 supply chain managers operating across different companies in Brazil. Structural equation modelling (SEM) and mediation techniques were employed for analysis. Findings Managers appear to acknowledge collaboration as relevant to risk management, with the role of the tacit knowledge reflected in managerial understanding being a key finding. SEM indicated that decisions in risk scenarios are guided by prior experience more than by collaborative logic. Mediation analysis revealed that, although valued, collaboration does not directly determine mitigation actions, particularly in disruptive contexts. The weak mediated correlation suggests that, in practice, the link between these variables may weaken under pressure, leading managers to make individualized decisions and responses. Originality/value A multidisciplinary approach was adopted to examine the effects of the pandemic on risk management and collaboration in supply chains, challenging established managerial practices and existing theories. The integration of behavioural and heuristic variables highlights the importance of situational awareness, knowledge management and cognitive flexibility in decision-making within complex contexts.

Purpose This paper aims to understand the role that accounting inscriptions play in mediating uncertainties between a central company and other supply chain actors in anticipation of a novel geopolitical risk. Design/methodology/approach An exploratory comparative analysis is conducted on three large multinational food companies, each of which faced significant supply chain challenges arising from the threat of Brexit. Brexit refers to the United Kingdom's (UK's) decision to leave the European Union (EU), following a referendum, resulting in negotiations and substantial changes in trade relations between the UK and the EU. Findings The findings reveal three distinct supply chain risk management pathways, shaped by the calculative cultures of the central companies and the actors they aim to engage with through mediation. When the central company prioritises mediating actions from suppliers and the government, and is sceptical of calculations, risk talk is primarily used. When mediation involves various supply chain actors, and the central company adopts a pragmatic approach to calculations, risk tools are integrated with risk talk to enable cohesive discussions. Finally, when the focus is on internal mediation between the finance and supply chain procurement functions, and the central company adopts a calculative idealist approach, familiarity with risk tools makes these tools central to the mediation process. Originality/value The authors adopt a comparative ex-ante field study perspective to (1) contextualise central company decisions within this uncertainty setting, illustrating how calculative cultures shape adjustments in mediating instruments and (2) theorise how a broad set of accounting inscriptions, including both risk tools and risk talk, can be used and combined in stimulating inter- and intra-organisational mediation under supply chain uncertainty.

Soybeans are a strategic commodity in global agricultural trade, and disruptions to their pricing system have direct implications for food security and trade patterns. This paper examines how major external shocks, particularly the Sino–U.S. trade wars, reshaped the dynamic connectedness and risk transmission structure of the global soybean price system. Using daily data from 2015–2025 for five key benchmarks, Chicago Board of Trade (CBOT) soybean futures, Dalian Commodity Exchange (DCE) No. 1 soybean futures, and cost-and-freight (CNF) prices for U.S. Gulf, Brazil, and Argentina shipments to China, we apply the time-varying parameter vector autoregression Diebold–Yilmaz connectedness model (TVP-VAR-DY) and the time-varying parameter vector autoregression Baruník–Křehlík frequency connectedness model (TVP-VAR-BK) models to quantify time-varying spillovers across short-, medium-, and long-run horizons. The results indicate that the global soybean market is highly integrated, while systemic risk transmission is predominantly short-run and declines sharply at longer horizons. CBOT futures remain the principal source of spillovers, especially in the short term, yet their net influence weakens noticeably after the 2018 trade-friction episode and declines further following the 2025 episode, particularly with respect to South American CNF benchmarks. Frequency-specific evidence suggests that trade-policy escalations are increasingly priced as structural shocks, strengthening medium- and long-horizon connectedness, while DCE’s outward spillovers rise markedly around 2025, consistent with the emergence of a more regionalized pricing architecture centered on Chinese demand. Within South America, Brazil leads short-run price formation, whereas longer-horizon dynamics are more exposed to Argentine policy risk spillovers. These findings provide new evidence on supply-chain reconfiguration and benchmark rebalancing in global soybean pricing and offer policy implications for strengthening China’s pricing capacity and enhancing multi-horizon supply-chain risk management.

ABSTRACT ESG rating disagreement has become increasingly prevalent, yet its cross-firm spillover effects along supply chains remain underexplored. Using matched data of Chinese A-share listed companies and their suppliers from 2009 to 2024, this study employs panel fixed-effects models combined with instrumental variable approaches, propensity score matching, and other robustness checks to empirically examine the impact of supplier ESG rating disagreement on customer firm resilience. The results indicate that supplier ESG rating disagreement significantly undermines customer firm resilience. Mechanism analysis reveals that this negative effect operates through three channels: heightening reputational pressure, exacerbating financing constraints, and intensifying supply chain relationship volatility. Heterogeneity analysis demonstrates that this adverse effect is more pronounced among non-state-owned enterprises, firms with higher supplier concentration, those operating in environments with greater retail investor attention to environmental issues, and firms in more competitive industries. This study uncovers the cross-firm spillover effects of ESG rating disagreement along supply chains, offering policy implications for ESG rating standardization and supply chain risk management.

Today, turbulence in the economic environment creates numerous challenges across various aspects of firms, requiring organizations to respond quickly to survive and grow. As a result, supply chain flexibility (SCF) has become a strategic priority for companies and a prevalent topic in academia. Grounded in Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study examines how the business environment (ENVI) drives digitalization in operations management activities (DOM) and how DOM enhances SCF both directly and indirectly through supply chain visibility (SCV), supply chain collaboration (SCC), and supply chain risk management (SCRM). In this research, we collected data from 289 Vietnamese firms and analyzed them using PLS-SEM. We checked the reliability and discriminant validity of the measurement scales, then predicted the effect sizes and mediation effects of the relationships. The results show that ENVI significantly drives DOM adoption, which in turn improves SCV, SCC, and SCRM, thereby strengthening SCF. The findings reveal that supply chain visibility and risk management, but not collaboration, mediate the DOM–SCF relationship and function as sequential pathways through which environmental turbulence translates into greater supply chain flexibility. Furthermore, the research offers managerial insights into leveraging digital tools to construct more adaptable and resilient supply chains in volatile economic environments.

Purpose This study aims to examine the integrated impact of artificial intelligence (AI)-powered supply chain risk management (AISCRM), autonomous resilience (AR), organizational sensemaking (OS) and supply chain resilience (SCR) on enhancing US manufacturing supply chain resilience during digital transformation, thereby addressing a critical gap in the current literature. Design/methodology/approach Utilizing a quantitative approach, the research collected data from 741 respondents across 27 US states, representing 100 manufacturers in automotive, aerospace, electronics and industrial manufacturing sectors. The study used systematic random sampling via SurveyMonkey, distributing questionnaires to 1,900 employees. Structural equation modeling was used to test hypotheses linking AISCRM, AR, OS and SCR, grounded in the dynamic capability view (DCV). Findings The results confirm that AISCRM has a positive influence on AR (H1); AR enhances OS (H2) and SCR (H3); OS has a positive effect on SCR (H4); and OS mediates the relationship between AR and SCR (H5). These findings highlight the synergistic role of AI-driven autonomy and human sensemaking in fostering resilience, with significant implications for diverse manufacturing contexts. Practical implications Manufacturers can leverage AISCRM and AR to enhance autonomous recovery, while OS can guide strategic adaptations to reduce the $50bn annual loss from supply chain disruptions. This provides actionable strategies for large, digitally intensive manufacturers to enhance their competitiveness. Originality/value To the best of the authors’ knowledge, this research is among the first to empirically integrate AISCRM, AR, OS and SCR, addressing a gap identified in recent studies, providing a holistic framework for resilience in the digital era, and contributing to both theory and practice in manufacturing supply chain management.

Purpose This article investigates the influence of marketing and organizational innovation practices on supply chain risk management (SCRM) capabilities, namely resilience and robustness, and examines both their direct and indirect effects on enhancing overall SCRM performance. Design/methodology/approach This is explanatory research using a deductive approach. This study uses survey data from 267 manufacturing export firms analysed through partial least squares structural equation modelling. Findings The findings suggest that marketing innovation (MI) and organizational innovation (OI) practices significantly strengthen supply chain risk management (SCRM) capabilities, specifically robustness and resilience, which subsequently exert a substantial influence on enhancing SCRMP. Beyond these direct relationships, the analysis further indicates that MI contributes to SCRMP indirectly through both resilience and robustness, demonstrating partial mediation, whereas the effect of OI on SCRMP is realized exclusively through resilience and robustness, thereby evidencing full mediation. Originality/value This article contributes to the supply chain management (SCM) literature by linking innovation and supply chain risk management (SCRM), an area that has only recently gained attention. To the best of the authors’ knowledge, this study offers one of the first comprehensive examinations of how marketing innovation (MI) and organizational innovation (OI) relate with existing strategies to build resilience and robustness as core SCRM capabilities. In doing so, it contributes to both theory and practice by providing a broader perspective on the mechanisms through which innovation supports sustained SCRM performance.

This study examines the impact of digital transformation on supply chain efficiency and risk management in the oil and gas industry. By integrating technologies such as artificial intelligence, Internet of Things, and big data analytics, supply chains are becoming more efficient, transparent, and resilient. The findings indicate that digital transformation significantly improves operational performance, reduces costs, and enhances decision-making. However, it also introduces new risks, particularly related to cybersecurity and system dependency. The study concludes that sustainable supply chain performance requires a balanced integration of digital technologies and risk management strategies.

A dynamic risk assessment framework is proposed to address the complex challenge of concurrent logistics disruptions and policy compliance issues in cross-border supply chains. The framework integrates the Light Gradient Boosting Machine (LightGBM), the Multi-Scale Graph Attention (MGAT) network, and the Temporal Convolutional Network (TCN). This integration effectively captures the nonlinear coupling relationship between the evolving network topology and multimodal data. Utilizing order data from a Brazilian retailer, the model extracts node embedding features and time-series features through an incremental learning mechanism. Furthermore, hierarchical Shapley value analysis identifies critical risk transmission paths, and a deep reinforcement learning strategy engine optimizes risk mitigation solutions. The experimental results show that the model achieves a prediction accuracy of 92.5%, significantly outperforming traditional methods. The proposed strategy engine reduces the risk response time to 15 min, effectively mitigating risks while maintaining operational cost control.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-026-47327-7.

Novel causal decision models for human-centric virtual reality platform selection in supply chain risk management

This study examined the impact of Supply Chain Risk Management (SCRM) practices on supply chain resilience. It was guided by three key questions: (1) What timely risk identification practices enhance resilience? (2) What monitoring practices strengthen resilience? and (3) What risk treatment strategies support resilient outcomes? A descriptive research design with a quantitative approach was adopted. The study targeted heads of departments, procurement and stores managers, administrative officers, production managers, project managers, and finance officers. Using purposive sampling, 40 respondents were selected from a population of 60, with 39 valid responses collected through self-administered questionnaires. The data were coded and analyzed using SPSS. Findings revealed that effective supply chain risk management is crucial to an organization's survival and resilience. Firms that identify risks early are better positioned to maintain control, allocate resources efficiently, enhance agility, and implement timely mitigation strategies. Continuous risk monitoring was found to improve flexibility, visibility, collaboration, and overall responsiveness. Similarly, structured risk treatment practices strengthened recovery capacity, resistance, redundancy, trust, financial stability, and competitive advantage. The study concludes that the timely and systematic implementation of risk identification, monitoring, and treatment practices significantly enhances supply chain resilience and organizational performance. It recommends that organizations adopt cost-effective, process-driven risk management systems, promote timely information sharing among stakeholders, decentralize risk-related decision-making, and foster a strong risk management culture. Further research with larger samples is encouraged to deepen the understanding of emerging dimensions of supply chain resilience.

As global supply chains become increasingly complex, it is vital to enhance the resilience of semiconductor manufacturing supply chains for sustainable development. We focus on the strategic choice of the semiconductor manufacturer to improve resilience by opening the supply chain risk management (SCRM) platform and sharing its capabilities. In practice, semiconductor manufacturers typically procure semi-finished products from semiconductor suppliers, who also compete with manufacturers in the downstream market. This coopetitive relationship introduces additional complexity into the manufacturer's sharing decision. To analyze this dynamic, we develop a game-theoretic model to analyze the optimal sharing decision. First, our study finds that even when the revenue from the supplier's access is lower than the costs incurred from access, the semiconductor manufacturer sometimes still opens the platform. Then, the semiconductor manufacturer may invest more in the platform even when the product value is relatively low. Additionally, while accessing the platform improves the supplier's technical performance, capability sharing constitutes a win–win strategy only under certain conditions. Furthermore, capability sharing may lead to improvements in both customer surplus and social welfare. Our findings provide valuable insights for semiconductor manufacturers' decisions on SCRM capability sharing, suppliers' response mechanisms, and relevant policy formulation.

ABSTRACT Supply chain risk management has drawn increasing attention as individual firms no longer compete as isolated entities in the tourism industry. Nevertheless, very few studies have systematically investigated the risks associated with the tourism supply chain. This study uses systems theory to develop a conceptual framework for tourism supply chain risk management. Specifically, it employs a holistic hierarchical approach to identify, prioritize, assess, and manage the potential risks in the tourism supply chain. It proposes that complex tourism supply chain risks can be identified and assessed by analyzing supply chain sub-systems using with structural decomposition. The risks associated with each sub-system contribute to and affect the risks of the overall supply chain system. The research findings suggest that the systems approach can effectively identify and examine the scope and scale of potential risks in the tourism supply chain.

Purpose This conceptual paper examines how artificial intelligence (AI) can be systematically integrated into supply chain management (SCM) training to support sustainable management objectives. It focuses on how AI-enabled training can enhance strategic adaptability and risk-aware decision-making to increase operational efficiency in increasingly volatile and sustainability-oriented supply chains. Design/methodology/approach This study adopts an integrative literature-based approach, drawing on publications from 2010–2024 across the SCM, AI, and training and development domains. Guided by the resource-based view, transaction cost economics and the dynamic capabilities framework, the paper synthesizes this literature into thematic clusters and develops a conceptual framework for AI-driven supply chain training. The review process follows a PRISMA-inspired structure to transparently report the identification, screening, eligibility and inclusion of relevant studies. Findings The analysis reveals that AI-enabled training, through simulations, real-time decision-making exercises, and predictive analytics, can enhance dynamic capabilities, reduce coordination and information costs, and foster data-driven competencies. These mechanisms collectively enhance supply chain resilience, risk management, and sustainability performance. The paper identifies five strategic insights that connect AI capabilities to training design, demonstrating how AI can move SCM training from static, process-oriented models toward adaptive, learning-oriented architectures. Originality/value This paper contributes by bridging classical SCM theories and AI-enabled training, a relatively underexplored area in supply chain education. By bridging the gap between traditional SCM theories and the practical needs of modern digital operations, the paper offers novel insights into AI’s transformational potential in enhancing supply chain adaptability and managerial decision-making. Additionally, it sets a foundation for future empirical research to validate and expand upon the proposed theoretical constructs.

The epitome of sustainable public procurement practices aligns with the principles of transparency, integrity, competitiveness, and efficiency, underpinned by the growing adoption of big data analytics capabilities. Big data analytics (BDA) enhances supply chain visibility, operational efficiency, and risk management; however, it has become evident that public supply chains in developing nations require robust digital infrastructure to effectively leverage the potential benefits of BDA. The research objective is to establish the effective role of Big Data Analytics (BDA) in enhancing sustainable public procurement practices (SPPP), aligning with the objectives of the Namibian Procurement Act 2015. The research further evaluates the factors impacting the technological, organisational and environmental context of public procurement principles through data-driven insights on the view of economic constraints and inequalities. The descriptive quantitative research design is rooted in a positivist perspective, focusing on an empirical case study of Namibia to assess the impact of BDA within the Technology-Organisation-Environment (TOE) framework. The research design employs a descriptive case study, enabling the exploration of factorial interrelationships. Diverse perspectives are integrated via stratified sampling, resulting in a sample size of 270 administered for data collection. Inferential statistics, including multivariate analysis, were used to analyse the data. The study revealed enhanced public procurement practices following the enactment of the Public Procurement Act 2015. ICT infrastructure, data quality, and system scalability are pivotal for BDA integration, despite hindrances such as leadership commitment, procurement capacity, procedural complexities, and resource constraints, which impede the shift towards sustainable procurement. These findings culminate in a TOE framework, which suggests that BDA's adoption offers significant strategic value, including improved decision-making, increased efficiency, enhanced transparency, and improved supplier performance in procurement. BDA's strategic effective role/value influences its adoption, leading to the transformation of sustainable public procurement practices in Namibia. This model systematically outlines the impact of BDA adoption on public procurement, showcasing how the TOE factors shape BDA's success in achieving SPPP. This research provides policymakers, public procurement professionals, and technology providers with practical insights to enhance the strategic value of BDA for sustainable procurement. This study contextualises the interplay of TOE factors for BDA adoption in public procurement for developing nations.

A machine learning-based medical device recall initiator prediction framework: From supply chain risk management and resilience view

Cyber supply chain risk management (C-SCRM) programs must consider operations that depend on the lifecycles of digital components such as hardware, firmware, software, and services. We integrate academic literature, historical incidents, and existing standards to identify obstacles faced by C-SCRM programs.

Blockchain technology for risk management in food supply chain: A systematic literature review on emerging themes and sustainability implications

• Review of 98 papers and 161 incidents to analyze software supply chain trends. • Developed taxonomy of 19 software supply chain attack types, including newly explored attacks. • Mapped 25 controls to attack types, boosting software supply chain protection. • Synthesized approaches for vendor software supply chain risk management. • Advanced software supply chain security with structured attack frameworks. The software product is a source of cyber-attacks that target organizations by using their software supply chain (SSC) as a distribution vector. As the reliance of software projects on open-source or proprietary modules is increasing drastically, SSC is becoming more and more critical and, therefore, has attracted the interest of cyber attackers. While existing studies primarily focus on software supply chain attacks’ prevention and detection methods, there is a need for a broad overview of attacks and comprehensive risk assessment for software supply chain security. This study conducts a systematic literature review to fill this gap. By analyzing 96 papers published between 2015-2023, we identified 19 distinct SSC attacks, including 6 novel attacks highlighted in recent studies. Additionally, we developed 25 specific security controls and established a precisely mapped taxonomy that transparently links each control to one or more specific attacks. By establishing this relationship, we demonstrate how SSC security controls are strategically designed to counteract specific attack vectors. Furthermore, we emphasize the role of risk assessment as a foundational step in understanding and prioritizing these vulnerabilities. This study introduces a risk assessment methodology tailored to software supply chain environments, focusing on identifying vulnerabilities in software components, dependencies, and suppliers. The proposed methodology enables organizations to systematically prioritize threats and implement appropriate mitigation strategies.