Search Results (1,248)

Against the background of global green transition and industrial chain restructuring, the new energy vehicle (NEV) industry chain faces systemic challenges, including high resource dependence, technological constraints, and geopolitical risks. It is therefore necessary to build a sustainable resilience framework that reflects security, controllability, green development, and long-term transformation. Drawing on the resource-based view, dynamic capability theory, institutional theory, and national innovation system theory, this study constructs an integrated indicator framework based on four-dimensional capabilities and a three-level structure. The framework includes four dimensions, namely resistance, adaptive recovery, autonomous controllability, and sustainable innovation, and three structural levels, namely the node, chain, and network levels. A total of 23 secondary indicators are developed. Using the Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Modeling (DEMATEL–ISM) method and scoring data from 15 industry experts, this study systematically examines the influence relationships and hierarchical structural relationships among the indicators. The results show that sustainable resilience in the NEV industry chain is not shaped by a single capability, but by the structural coordination among basic protection, adaptive recovery, autonomous controllability, and sustainable innovation. Autonomous controllability occupies a core linkage position in the framework, while network-level indicators provide important foundational support across different dimensions. This study further suggests that resilience improvement should move beyond short-term emergency response and place greater emphasis on long-term capability building, including supply security, coordinated recovery, technological autonomy, and green innovation governance. The findings provide theoretical insights and practical references for strengthening the security, controllability, and sustainability of the NEV industry chain. 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

Recent global disruptions have exposed the fragility of tightly coordinated supply chains, particularly under transportation and information delays, motivating the need for analytical tools to assess their stability limits. This study analyzes a two-echelon supply chain system to determine how delays affect stability and performance, with an emphasis on the role of feedback coordination. A continuous-time delay-differential modeling framework was developed to examine both uncoupled and coupled configurations. Stability is analyzed through characteristic equations, and explicit closed-form expressions for the critical delay threshold are derived as functions of the coupling gain and shipment rate. The uncoupled system is shown to exhibit delay-independent marginal stability but lacks the ability to regulate downstream inventory. In contrast, the coupled system achieves inventory regulation but introduces delay-dependent stability with a critical delay, beyond which oscillations grow unbounded. A key result revealed an inverse relationship between coupling strength and delay tolerance, highlighting a trade-off between responsiveness and robustness. An optimal control formulation further demonstrates that the stability constraints limit the achievable performance. These findings provide a theoretical explanation for the vulnerability of just-in-time systems and offer practical guidelines for resilient logistics design, enabling supply chain practitioners to quantify stability margins and balance coordination efficiency with robustness to transportation delays. Full article

Promoting the coordinated development of digitalization and greening has become an important pathway for firms to achieve high-quality growth. Using panel data for A-share listed firms in China’s Yangtze River Basin from 2010 to 2022, this study examines the effect of supply chain innovation policy on firms’ digital–green development. We measure the synergy between digitalization and greening using a composite system synergy approach and identify the policy effect through a quasi-natural experiment based on the supply chain innovation policy, combined with a synthetic difference-in-differences model. The results show that the policy significantly improves the coordinated development of firm digitalization and greening, and the findings remain robust across a series of tests. Mechanism analysis indicates that this effect operates through three channels: easing financing constraints, increasing supply chain diversification, and promoting industrial chain modernization. Moderating effect tests further show that supply chain efficiency, supply chain resilience, and entrepreneurship strengthen the policy’s positive effect on digital–green development. Heterogeneity analysis suggests that the policy effect varies systematically with firm size, market competitiveness, and information asymmetry. This study provides micro-level evidence on how supply chain innovation policy can promote firms’ digital–green transformation and offers useful implications for policies aimed at improving firm competitiveness and supporting sustainable development. Full article

Against the backdrop of frequent global shocks and increasingly complex supply chain networks, supply chain disruption risk exposure has become a major challenge affecting firms’ operational stability and sustainable competitive advantage. Meanwhile, generative artificial intelligence is being increasingly embedded in business operations and has demonstrated strong application potential in information processing, risk identification, and decision support. Based on data from Chinese A-share listed firms from 2017 to 2024 and using text measures based on Management Discussion and Analysis (MD&A) disclosures of Generative AI application and supply chain disruption risk exposure, this study examines the relationship between Generative AI application and corporate supply chain disruption risk exposure, and further explores the channels through which this relationship may operate from the perspective of risk governance transformation. The results show that Generative AI application is significantly associated with lower corporate supply chain disruption risk exposure, and this relationship remains robust across a series of robustness checks and supplementary endogeneity analyses. Channel analyses suggest that this relationship may be related to firms’ risk governance transformation, mainly reflected in enhanced risk identification capability, improved resource allocation capability, and strengthened collaborative response capability. Heterogeneity analyses show that this association is more pronounced among firms facing higher environmental uncertainty, manufacturing firms, and firms located in cities with lower entrepreneurial vitality. This study provides text-based firm-level evidence for understanding the relationship between Generative AI application and supply chain risk governance, and offers managerial implications for firms seeking to promote scenario-based Generative AI application and enhance supply chain resilience and risk governance capability. Full article

Background: Agricultural logistics in Kazakhstan is critical for export-oriented supply chains, but its resilience is limited by infrastructure constraints, fluctuating export demand, and insufficient coordination between market and logistics processes. Methods: This study develops a conceptual multi-level model of the agricultural logistics system and a hybrid simulation model combining system dynamics and discrete-event simulation to analyze intermodal transportation under demand and capacity constraints. The model integrates demand formation, storage, transport, and export operations, as well as feedback mechanisms between fulfilled demand, repeat orders, and logistics performance. The model is implemented in AnyLogic 8.9. Results: The conceptual model structures the interaction of key participants, logistics facilities, and infrastructure levels within Kazakhstan’s agricultural logistics system. Simulation experiments reproduce cyclic logistics behavior and show that reduced logistics capacity increases the demand gap and system pressure, while stronger market signals intensify demand and infrastructure load. The results confirm that resilience depends on the balance between demand activation, logistics capacity, and replenishment policy. Conclusions: The proposed approach provides a tool for analyzing the resilience of agricultural intermodal logistics in Kazakhstan and supports scenario-based evaluation of infrastructure and market factors. The novelty lies in combining a conceptual multi-level logistics model with hybrid simulation of demand and logistics flows. Full article

Post-conflict reconstruction concentrates institutional fragility, supply-chain disruption, and weak regulatory enforcement at the moment when long-term resilience trajectories are being set. Myanmar’s housing sector, operating under prolonged civil conflict and post-earthquake reconstruction pressure, exemplifies these conditions. This research adapts Singapore’s Buildable Design Appraisal System (BDAS) and Constructability Appraisal System (CAS) to Myanmar’s post-conflict housing context and translates the empirical findings into a Knowledge-Based Decision Support System (KBDSS). An integrated framework combining Value Chain Analysis (VCA), the Technology Acceptance Model (TAM), and Scott’s Institutional Framework (IF) underpins the study. A questionnaire survey (n = 139) of Myanmar building professionals is analysed using Partial Least Squares Structural Equation Modelling and Necessary Condition Analysis. The model explains 57.9% of the variance in framework adaptation; competitive advantage, perceived usefulness, perceived ease of use, and the post-conflict/disaster context emerge as both sufficient and necessary conditions, while regulative support dominates among the three institutional pillars. These findings underpin the inference logic of a prototype KBDSS for resilient housing reconstruction. This research contributes empirical evidence on operationalising urban resilience under institutional fragility in the Global South. Full article

Grounded in dynamic capability theory, this research examines the impact of AI-enabled logistics optimization (ALO) on logistics process efficiency (LPE) and sustainable supply chain performance (SSCP). It further explores the mediation of LPE and the moderation of environmental uncertainty (EU). A structured online questionnaire was distributed to 600 participants via stratified random sampling from June to December 2025, resulting in 380 valid responses, and was analyzed using structural equation modeling. The results include a significant influence of ALO on LPE and SSCP. In addition, LPE significantly affects SSCP and partially mediates the ALO–SSCP relationship. Additionally, the EU significantly moderates the ALO–SSCP relationship, identifying that ALO becomes more performance-related under a volatile and uncertain operational environment. The research is based on cross-sectional survey data with self-reported outcomes. Future research is recommended to employ longitudinal or multi-source research methods. It also suggested examining other mechanisms for dynamic capabilities (e.g., agility, resilience) across several sectors. The results of this research extend dynamic capability by elucidating when (under the EU) and how (via LPE) ALO transforms into SSCP. Full article

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation. Full article

This study aims to address the need to operationalize Industry 5.0 (I5.0) by developing a comprehensive Assessment Framework for the adoption of the Human Centricity, Environmental Sustainability, and Industrial Resilience pillars. While existing models largely focus on technological maturity, they fail to provide measurable tools for evaluating I5.0 adoption. To bridge this gap, the paper proposes an Assessment Framework based on a structured set of Key Performance Indicators (KPIs) developed within the EU-funded PROSPECTS 5.0 project. The methodology combines an extensive literature review, a workshop with relevant stakeholders, a Delphi survey with experts, and empirical refinement conducted through workshops involving 14 companies across multiple sectors and of varying sizes. The results highlight that organizations predominantly measure traditional indicators such as health and safety, energy consumption, and supply chain robustness, while underestimating emerging dimensions such as human empowerment, social inclusion, circularity, and advanced human–machine collaboration. The framework introduces a set of KPIs for each of the I5.0 pillars, supporting structured assessment across different industrial contexts while allowing sector-specific adaptation. The findings reveal a gap between the perceived importance of several sustainability and human-centric metrics and their actual implementation. This framework allows organizations to self-assess their practices, guide strategic decisions, and align technological growth with societal and environmental goals. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

In the digital era, intelligent applications play an increasingly pivotal role in restructuring supply chain cost management. Using panel data from Chinese-listed firms between 2010 and 2024, this study examines the impact of customers’ Artificial Intelligence (AI) adoption on the cost stickiness of their suppliers. The findings indicate that customers’ AI adoption mitigates suppliers’ cost stickiness. This effect is more pronounced for larger suppliers, those with shorter geographic distance to customers, and those in highly competitive industries. Furthermore, customers’ AI adoption alleviates suppliers’ cost stickiness by promoting flexible production modes, enhancing production information flexibility, and raising production efficiency. Moreover, a two-stage model suggests that this alleviation of cost stickiness enhances suppliers’ corporate resilience, offering directional insights for transmitting within supply chain systems. In summary, this paper expands the theoretical understanding of intelligent applications in supply chain systems, by substantiating cross-firm spillover effects and interactive behaviors among supply chain stakeholders. Full article

Against the backdrop of increasingly stringent environmental regulation and increasing uncertainty in supply chain operations, this study examines how environmental penalties affect corporate supply chain resilience. Using Chinese A-share listed firms from 2009 to 2024, this paper constructs a firm-level panel dataset and employs a two-way fixed-effects model to estimate the relationship between environmental penalty intensity and supply chain resilience. Environmental penalty intensity is measured by the annual penalty amount imposed on each firm, while supply chain resilience is captured through an entropy-weighted index reflecting both resistance and recovery capacities. To alleviate endogeneity concerns, this study further uses an instrumental-variable approach based on the interaction between a firm’s one-year lagged penalty amount and city-level thermal inversion days. The results show that environmental penalties reduce corporate supply chain resilience. This negative effect is heterogeneous across firm characteristics and is partially mediated by reduced operational efficiency and crowded-out R&D investment. This conclusion remains robust after replacing the dependent variable, changing the clustering level of standard errors, and excluding observations from the COVID-19 pandemic period. Mechanism tests suggest that environmental penalties weaken supply chain resilience partly by reducing operational efficiency and crowding out R&D investment. Heterogeneity analysis indicates that the negative effect is more pronounced among young firms, non-high-tech firms, and firms located in regions with lower environmental regulation intensity. This study contributes to the literature by distinguishing environmental penalties from broader environmental regulation and by examining their implications for supply chain resilience. The findings also suggest that environmental enforcement should maintain deterrence while improving transparency, predictability, and targeted compliance guidance. Full article

In light of the growing prominence of environmental issues, the frequent occurrence of unexpected incidents, and the dynamic challenges of a changing market environment, suppliers must possess comprehensive capabilities that encompass both green and sustainable development as well as resilience to risks. Consequently, green supplier selection has emerged as a critical research topic. By integrating virtual and physical systems, digital twin technology enhances supply chain transparency and efficiency—a capability that plays a significant role in advancing sustainable supply chain development. In view of this, this study incorporates risk factors into the green supplier evaluation system, introduces indicators related to digital twin technology, and proposes a stochastic slack-based measure data envelopment analysis method, namely SSBM, for evaluating green suppliers. This approach expands and refines the existing evaluation criteria and the decision-making model. Finally, a numerical case study is conducted to validate the feasibility of the proposed method. This research provides more systematic and scientific decision support for green supplier selection, enriching the theoretical and practical applications in the fields of green supply chain and multi-criteria decision-making. Full article

International agri-food trade and climate change interact in ways that have significant implications for supply chain resilience and food sovereignty, yet these interactions remain insufficiently understood at the level of specific traded commodities. This paper analyses European fresh orange imports over 2012–2022 using a unit value decomposition applied to FAOSTAT and Eurostat bilateral trade data, alongside a seasonal supply analysis of monthly import flows from the main exporting regions. The analysis documents a pronounced geographic reorientation of global orange production toward developing and emerging economies in North Africa, Southern Africa, and South America, many of which face documented climate-related stressors. The unit value decomposition identifies how exporter-level unit values and import share reallocations contribute to changes in regional import unit value indices. The seasonal supply analysis shows that the European orange supply depends on a tight sequence of regional exporters operating in largely non-overlapping seasonal windows, leaving limited redundancy if disruptions occur in any single supplying region. These findings provide a descriptive, origin-disaggregated account of Europe’s trade-side exposure in fresh orange supply chains. They underscore the need for product-specific monitoring tools and policy approaches that consider seasonal import dependence, supplier concentration, and the climate vulnerability of major origin regions, while recognising that the present analysis does not estimate causal climate effects. Full article