Search Results (737)

Artificial intelligence applications are rapidly integrated into supply chain management systems, and play a vital role in coping with supply chain disruption risks from external shocks. From the perspective of a production network, based on the data of Chinese listed companies from 2013 to 2023, this paper systematically examines the direct effect and network spillover effect of artificial intelligence application on supply chain disruption risk. The results show that: First, artificial intelligence applications can significantly reduce supply chain disruption risks. The adoption of AI by upstream enterprises produces spillover effects through production networks, and indirectly cuts down the disruption risks of downstream enterprises. Second, AI applications mainly function through three paths: supply chain concentration, corporate agency cost and physical flow efficiency of enterprises. Third, the risk reduction effect of AI applications is more prominent in eastern regions, low-tech industries and state-owned enterprises. The research conclusions have important theoretical and practical implications for enterprises to build risk management systems and enhance system resilience. Full article

Selecting suppliers for circular supply chains is an important requirement, demanding evaluation frameworks that capture reuse, reverse flows, and waste minimization beyond traditional metrics. This paper introduces a structured model designed to assess suppliers against specific circularity-oriented criteria. The Best-Worst Method (BWM) derives criteria weights, the Analytic Hierarchy Process (AHP) ratings evaluate alternatives, and a risk assessment stage consolidates the final ranking. The primary insights of this research include: (i) the development of a structured supplier evaluation model that encompasses dimensions like closed-loop integration, end-of-life management, material efficiency, and waste management into a multi-criteria perspective; (ii) applying BWM to derive consistent criteria weights, clarifying how circular performance attributes shape supplier prioritization; (iii) applying AHP ratings and risk assessment to consolidate the evaluation into a final ranking of alternatives; and (iv) demonstrating the operational feasibility and applicability of the framework through a real-world case analysis, providing empirical evidence for assessing circular supplier performance in industrial environments. Full article

Sustainable supply chains are increasingly vulnerable to systemic risks, such as geopolitical conflicts at critical trade routes like the Strait of Hormuz or climate disasters, which reveal deep Environmental, Social, and Governance (ESG) weaknesses. Conventional optimization often fails in these “deep uncertainty” contexts, where reliable historical data are often scarce and qualitative factors are paramount. This study introduces a simulation-optimization framework that reframes risk management as a decision process rather than a purely computational one. Portfolios are parameterized across five key characteristics—prevention, vulnerability, resilience, recovery, and detection—to enable a genetic algorithm (GA) to generate a diverse ensemble of high-performing strategies. Instead of providing one “best” answer, the GA allows managers to evaluate multiple options against quantitative tail-risk measures and qualitative institutional factors. The framework produces a “trade-off map,” or Pareto frontier, visualizing the cost of protecting against downside risks. By adjusting the GA’s settings, decision makers can toggle between improving current plans and exploring new, structurally different strategies. The numerical results demonstrate that the GA consistently identifies high-performing portfolios, achieving at least 99.55% of the true optimal performance across all metrics while requiring only 25% of the computational evaluation budget of an exhaustive search space. Furthermore, the framework successfully generates a structurally diverse menu of near-optimal alternatives across all performance metrics, consistently outperforming Monte Carlo sampling in the quality of near-optimal solutions identified, particularly for tail-risk measures such as conditional value-at-risk. Ultimately, this approach integrates the manager’s professional judgment regarding non-quantifiable factors, such as political stability and social responsibility, with simulation data to support the selection of a robust, sustainable portfolio. Full article

The scheduling of crude oil operations in marine refineries is a complex combinatorial problem, exacerbated by stochastic disruptions like vessel delays and port congestion. Traditional deterministic and expected-value approaches fail to mitigate high-impact tail events, causing severe demurrage and production bottlenecks. To address this, we propose a novel CVaR-Driven Reactive GRASP Simheuristic. This framework hybridizes GRASP with Monte Carlo simulation, embedding Conditional Value-at-Risk (CVaR) into the adaptive memory to actively steer the search away from catastrophic logistical gridlocks. Overcoming standard “unlimited port capacity” assumptions, the model endogenously calculates demurrage dynamics and introduces an automated Failure Taxonomy for explainable insights. Evaluated on a 30-day industrial case study, representing a standard short-term operational scheduling horizon, under baseline conditions and severe dynamic disruptions (vessel delays, unit maintenance), the diagnostic reveals that over 80% of scheduling failures stem from endogenous port congestion rather than internal dead-ends. Furthermore, a comprehensive ablation study mathematically validates the superiority of the CVaR-driven memory over standard expected-cost optimization in preventing catastrophic tail-risk scenarios. Results demonstrate that this CVaR-driven approach effectively absorbs stochastic shocks, prevents stockouts, and minimizes worst-case costs, generating highly robust schedules in under three minutes. Ultimately, it provides a robust, risk-aware Decision Support System (DSS) for supply chain and operations managers. Full article

Accurately detecting viable foodborne pathogenic bacteria is essential for food safety risk assessments and public health interventions. Traditional plate counting is time-consuming and operationally cumbersome. Immunological assays are unable to distinguish viable from dead cells, whereas conventional nucleic acid amplification is often affected by residual DNA originating from dead bacteria. These limitations render conventional approaches inadequate for rapid and precise field detection. Functional nucleic acids (FNAs) offer a promising alternative for viability detection because of their high sensitivity, specificity, target diversity, and programmable integrability. This review provides a systematic overview of molecular recognition strategies and FNA-based detection technologies for identifying viable foodborne microorganisms. We categorize the biomarkers targeted by FNAs into nucleic acids, surface structures, and metabolic activities. Building on this categorization, we examine the core principles and technological evolution of primers, aptamers, DNAzymes, guide nucleic acids, and oligonucleotide probes in viability discrimination. We then outline the practical applications of these technologies across the food supply chain and discuss the remaining challenges and future directions in the field. Ultimately, this work provides a theoretical reference and practical guidance for ensuring food safety and advancing precise microbial risk management. Full article

Against the backdrop of rising global economic uncertainty and growing risks in agricultural supply chains, strengthening supply chain resilience has become essential for safeguarding national food security and advancing high-quality agricultural development. As a governance-optimizing force, the role of patient capital (e.g., through long-term credit and institutional investor participation) in shaping agricultural supply chain resilience merits systematic investigation. Drawing on panel data from 895 Chinese listed agricultural enterprises (2010–2024), this paper examines the association between patient capital and agricultural supply chain resilience and explores variables consistent with potential mechanisms. The results show that patient capital is positively associated with agricultural supply chain resilience, with evidence consistent with three possible mechanisms: improving internal management efficiency, advancing digital-intelligent transformation, and strengthening collaboration capabilities. Heterogeneity analyses reveal that this positive association is more pronounced among non-chain-leader enterprises, firms with low supply chain transparency, and those exhibiting a strong bullwhip effect. Further analysis indicates that strong ESG performance and high green total factor productivity strengthen the positive association between patient capital and agricultural supply chain resilience. This study provides enterprise-level evidence regarding the potential role of patient capital in promoting more efficient, modern, and sustainable agricultural supply chains. Full article

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN–PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs. Full article

This study examines how corporate resilience is developed through inclusive and sustainable cocoa partnerships within integrated value chain governance in Sulawesi, Indonesia. Using an interpretive qualitative multiple-case study design, the research compares PT Mars Symbioscience Indonesia in Luwu Timur and PT Papandayan Cocoa Industries (Barry Callebaut) in Polewali Mandar. Data were collected from January to May 2025 through semi-structured interviews with 21 actors representing corporate, intermediary, farmer, financial, and local government stakeholders, and were triangulated with company documents, policy texts, and the relevant literature. The data were analyzed thematically using NVivo, supported by process tracing and cross-case comparison. The findings show that both firms combine certification, traceability, procurement arrangements, monitoring, and knowledge transfer, but organize these instruments through different partnership architectures. Mars follows a vertically integrated capability-building model involving 4250 farmers and 17 trained collectors, whereas Barry Callebaut relies on an intermediary- and standards-centered model through PT Bumi Surya Selaras, involving 3125 farmers in 126 farmer groups. These findings suggest that inclusive and sustainable cocoa partnerships function not only as supply-chain coordination mechanisms but also as institutional arrangements for governing smallholder-based production resources and long-term supply sustainability. Across cases, these partnerships were reported and interpreted as supporting supply stability, cocoa bean quality improvement, and risk mitigation. This suggests that corporate resilience in smallholder-based cocoa value chains is co-produced through the integration of governance mechanisms, farmer capacity building, intermediary coordination, and sustainable resource management. Full article

The global trade of tin-containing products has become increasingly complex due to supply–demand imbalances, geopolitical risks, and high trade concentration. Ensuring supply chain stability is critical for sectors such as electronics. This study constructed a global tin trade network (2000–2024), applied complex network analysis, and developed a cascading failure model to assess structural vulnerability and simulate supply disruptions. Results showed a highly concentrated network, with China, the United States, and Germany acting as key hubs. China emerged as the largest exporter of tin-containing final products in 2024 (84.70 kt), while the United States was the largest importer (27.82 kt) in 2024. The electronics and machinery sectors were particularly vulnerable, exhibiting large avalanche sizes and deep propagation hierarchies, while home appliances and food packaging showed comparatively lower risks. Simulations further revealed that disruptions in major supplier countries, particularly China, could trigger cascading failures affecting 193 economies (80.1% of all trading partners). To improve resilience, this study highlighted the importance of supply diversification and inventory buffers, industry differentiation management, and real-time monitoring systems, which are essential for building a more robust and sustainable global tin trade network. Full article

Offshore geothermal energy and the reuse of decommissioned oil and gas platforms are emerging as linked pathways for reducing the carbon intensity of marine energy supply while extending the value of mature offshore assets. This review examines offshore geothermal development from a full-chain perspective that connects resource assessment, platform and wellbore reuse, heat extraction, medium- and low-temperature conversion, multi-energy coupling, techno-economic evaluation and environmental risk management. The paper first clarifies the resource logic of offshore geothermal systems, especially sedimentary-basin resources that spatially overlap with mature petroleum provinces. It then analyzes two principal engineering routes: the reuse of existing offshore platforms as energy hubs and the reutilization of abandoned wells as open-loop or closed-loop heat-extraction systems. The review finds that platform and wellbore reuse can reduce drilling demand, shorten offshore construction cycles and lower life-cycle environmental burdens, but engineering feasibility remains constrained by wellbore integrity, thermal losses, corrosion and scaling, platform life extension, regulatory liability and the limited availability of field-scale demonstration data. Coupling geothermal energy with offshore wind power, hydrogen production, OTEC and desalination can improve system stability and equipment utilization; however, standardized assessment boundaries and comparable cost models are still insufficient. Future research should focus on resource-engineering-economic integrated assessment, standardized reuse packages, long-term offshore reliability databases, corrosion-resistant material systems, auditable TEA/LCA models and risk-based regulatory frameworks. This review provides a technical basis for offshore geothermal pilot projects and for the low-carbon transformation of offshore oil and gas infrastructure. Full article

Dietary exposure to microplastics (MPs) has emerged as a significant concern; therefore, its implications for exposure characterization are presented in this study. The lack of standardized testing methods currently limits effective risk management. Determining how industrial operations contribute to the presence of these xenobiotics in the food supply chain is essential, even if environmental absorption is a recognized factor. The Food Microplastics Pyramid (FOMIC-Py), a novel hierarchical structure designed to correlate MP prevalence with industrial processing intensity, is presented in this study. The investigation suggests that technogenic inputs may represent important contributors to contamination by synthesising current literature and applying the model to regional food patterns, especially an Italian North-South scenario study. The method uses sensitivity analysis (Spearman’s ρ = 0.94) for statistical validation and classifies food items from primary commodities (Level 1) to ultra-processed items (Level 5). Mechanical abrasion and packaging interactions are recognized as the main vectors by the FOMIC-Py, which reveals a consistent accumulation of MPs across all five levels of industrial transformation. While FOMIC-Py reliably assesses particles over 1 µm, current analytical constraints regarding nanoplastics lead to a significant exposure underestimation. Consequently, rather than being an established predictive model of human target-organ dosage, the FOMIC-Py framework serves as a new exploratory, hypothesis-generating tool. The absolute exposure metrics should be evaluated cautiously owing to the underlying variability of worldwide MP extraction data, even if our statistical predictions indicate a consistent relative ranking hierarchy across contaminated food categories. These first screening criteria provide a uniform basis to direct future targeted sampling procedures and regulatory prioritization. Full article

Global value chains (GVCs) are undergoing significant reconfiguration in a context of slower trade growth, rising geopolitical tensions and repeated supply chain disruptions. This article examines how these pressures have shaped the sourcing geography of Spanish apparel imports between 1999 and 2023. Drawing on a panel of the 25 main supplier countries (625 country-year observations), it analyses the changing structure of sourcing through three restructuring dynamics widely discussed in the recent literature: nearshoring, diversification and friendshoring. The results show that diversification, rather than regionalization, has been the main response to recent disruptions. While Spain’s apparel sourcing has become less concentrated, this shift has not led to a sustained shortening of supply chains or to a clear reduction in dependence on Asia. Geopolitical alignment has limited explanatory power at the aggregate level, although it becomes more relevant among semi-proximity suppliers competing on the basis of speed, flexibility and political reliability. Overall, the findings suggest that post-pandemic restructuring in Spanish apparel is better understood as a selective form of risk management within an existing buyer-driven GVC than as a broad move toward nearshoring. Full article

This study examines whether broader supply chain insurance coverage is associated with lower preventive resilience investment through perceived managerial moral hazard. Drawing on moral hazard theory and supply chain resilience research, it tests a moderated-mediation model using survey data from 241 managers in manufacturing-intensive firms. PLS-SEM is used as the main estimator, and covariance-based SEM is reported as an estimator-sensitivity check. Results show that insurance coverage breadth is positively associated with moral hazard perceptions, moral hazard perceptions are negatively associated with preventive resilience investment, and preventive investment is negatively associated with perceived disruption impact. Moral hazard perceptions significantly mediate the coverage breadth–preventive investment relationship, while the direct effect is not significant. The total effect of insurance coverage breadth on preventive resilience investment is negative and significant. Firm-perceived insurer verification stringency is associated with a weaker coverage–moral hazard perception relationship, whereas supply chain visibility provides a smaller attenuation effect. Exploratory risk-type moderation is directional but inconclusive. This study offers evidence from an emerging-market manufacturing context and suggests that contractual verification may help preserve prevention incentives, without estimating causal treatment effects. Full article

The construction sector continues to experience elevated levels of insolvency, driven by an interplay of structural vulnerabilities and macroeconomic pressures, including supply chain disruptions and cost inflation. These challenges have been particularly prominent in Australia, especially among micro and small construction firms, which account for over 90% of reported insolvency cases. In 2024, the Australian construction sector contributed nearly one-quarter of all company insolvencies nationally. This study undertakes a comprehensive review of construction insolvency research, synthesising key themes, causes, early warning indicators, and mitigation strategies, while contextualising global insights using empirical evidence from the Australian construction sector. The methodology integrated systematic literature screening, scientometric analysis, and critical thematic synthesis with a descriptive and selective statistical examination of the Australian Securities and Investments Commission (ASIC) data, complemented by practice-informed insights. The review identified dominant research trajectories, centred on financial risk management, insolvency prediction models, project-level cost and governance risks, and emerging data-driven approaches. Empirical analysis revealed that inadequate cash flow (~16–20%), poor strategic management (~12–18%), and weak financial controls (~11–15%) consistently rank among the leading causes of construction firm failure over the last decade. Indicators such as non-payment of statutory obligations and deteriorating working capital are observed in over half of insolvency cases, highlighting persistent structural fragility. Although global strategic focus areas emphasised financial monitoring and early warning systems, practice-informed findings indicated that effective mitigation requires their operationalisation through capability development, early intervention tools, regulatory oversight, and stakeholder-informed support mechanisms. The study shows how global insolvency risk concepts align with Australian regulatory evidence and highlights the need to translate early-warning approaches into accessible tools and support mechanisms for micro and small construction firms. Full article

Global supply chains are increasingly exposed to multi-source uncertainties, ranging from geopolitical tensions to climate extremes, making the accurate and interpretable prediction of disruptions an urgent operational priority. Existing predictive models often rely on either shallow statistical learners, which struggle with high-dimensional interactions, or deep neural networks, which trade off interpretability for marginal performance gains. To address this gap, we propose an interpretable machine learning framework that couples a feature-attention mechanism with a gradient-boosted decision tree ensemble for early warning of shipment-level disruption events. First, a dedicated attention module is trained to assign importance weights to 14 heterogeneous risk factors, generating an interpretable feature ranking that highlights pivotal signals such as lead-time volatility and geopolitical risk. The reweighted features are then fed into a gradient boosting classifier, which effectively captures non-linear patterns and interaction effects. Evaluated on a publicly available dataset of 5000 international freight records available on Kaggle, the proposed framework achieves an AUC of 0.8213 (±0.0002 over three independent runs), matching the best-performing baseline (standard gradient boosting, 0.8212 ± 0.0001) and surpassing logistic regression (0.777), random forest (0.806), and a standalone feature-attention network (0.805). The attention module preserves full predictive accuracy while adding an interpretability layer that conventional black-box implementations lack. Notably, the framework preserves the predictive accuracy of gradient boosting while enhancing interpretability through attention-based feature ranking and dual-perspective importance analysis, achieving a precision of 0.770 and a balanced F1-score of 0.781. The convergence of attention-based interpretability and ensemble learning efficiency provides supply chain managers with a transparent decision-support tool—distinguishing genuine “tipping points” from “false alarms” and enabling targeted risk mitigation under deep uncertainty. Full article