Search Results (705)

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

Digital transformation is increasingly viewed as a strategic driver of operational efficiency, financial performance, and organisational resilience in rural transition economies. Existing research, however, largely assumes homogeneous digitalisation effects across firms while overlooking the structural conditions shaping integration efficiency. This study investigates the threshold relationship between supply chain integration and financial–economic performance using a threshold regression framework. The analysis is based on firm-level data from 80 agricultural, agritourism, and tourism-related firms operating in rural Northern Albania. Methodologically, the study combines Hansen’s threshold estimation with robust OLS and threshold logistic regression models, complemented by exploratory macro-level threshold analysis for Western Balkan economies. The findings reveal significant regime-dependent dynamics. Below the estimated socio-economic integration threshold, supply chain integration generates weak and statistically insignificant effects. Above the threshold, integration mechanisms produce substantially stronger financial and operational outcomes, indicating that digital transformation becomes economically productive primarily under sufficiently integrated organisational conditions. Additional diagnostics further show that highly integrated firms achieve superior coordination efficiency, resource allocation, and financial resilience. The study contributes to the literature by advancing a managerial-financial and coordination-based interpretation of digital transformation and its threshold performance effects in rural transition economies. 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

The security of new energy vehicle (NEV) industry chains has become a strategic issue for industrial competitiveness, the energy transition, and economic security. This study examines how digital economy capabilities jointly support NEV industry chain security across 30 provincial-level administrative regions in China. Drawing on Organizational Information Processing Theory and Dynamic Capability Theory, we conceptualize artificial intelligence capability (AIC), big data analytics capability (BDA), cloud computing infrastructure (CCI), and blockchain application level (BCL) as complementary information-processing and reconfiguration capabilities. We combine Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and Random Forest/SHAP analysis. The revised results show that AIC is a practically necessary condition for supply chain resilience, BDA is a necessary condition for achieving a high cybersecurity level, and BCL is a dimension-specific necessary condition for data security. Four sufficient configurational paths—technology-driven, data-driven, infrastructure-driven, and security-synergistic—lead to high comprehensive NEV industry chain security. Robustness checks using alternative calibration anchors and consistency thresholds show that the core configurations are stable. A revised machine learning specification using only digital economy predictors confirms the high relative importance of AIC. It also shows that the marginal contribution of AIC tends to flatten beyond the upper-middle range. The findings provide a configurational and regionally differentiated perspective on digital economy empowerment while avoiding overgeneralization beyond the Chinese provincial context. Full article

Under the background of the steady advancement of the dual-carbon goal and the increasing improvement of the green financial system, green supply chain finance is like a bridge that closely links the capital of the financial market and the low-carbon transformation of the real economy. The following article chooses A-shares traded enterprises from 2014 to 2024 as the study sample, adopts multi-dimensional empirical methods to study the association in green supply chain finance along with corporate emission levels, and analyzes its transmission mechanisms and heterogeneity. The findings demonstrate that green supply chain finance has a substantial inhibitory impact with enterprise emission levels, a finding that remains robust across a series of tests, including parallel trend tests, placebo tests, and propensity score matching (PSM). Mechanism analysis demonstrates that green supply chain finance can indirectly reduce carbon emission intensity by improving both financing levels and supply chain stability. Looking at heterogeneity, we find that the emission-reducing effect tends to be stronger among state-owned firms, non-heavy polluters, enterprises with higher total factor productivity, and enterprises that are more financially oriented. Our theoretical value lies in clarifying the direct relationship between green supply chain finance and micro-enterprise carbon emissions, identifying two differentiated intermediary transmission paths, and defining the boundary conditions of the policy role across multiple dimensions, thereby better coordinating and promoting the digital and low-carbon transformation of enterprises. Full article

Digital transformation (DT) reshapes supply chain ecosystems and promotes inter-firm synergistic development. Using a sample of 2417 focal firm–partner dyads of Chinese A-share listed firms from 2013 to 2023, we employ regressions with industry and year fixed effects and mediation analysis to examine how focal firms’ DT affects partners’ internal control (IC) quality. We find that focal firms’ DT enhances partners’ IC quality, robust to various tests (e.g., IV, PSM). Mechanism analysis reveals two distinct pathways: transformation contagion (focal firms’ DT drives partners’ synchronized DT) and management spillover (focal firms’ DT-driven control activities exported to partners). Heterogeneity analysis shows that the positive transmission effect is stronger in geographically distant or low-concentration supply chain relationships, as well as for focal firms with greater market power. This study extends research on IC determinants beyond firm boundaries and shifts DT externality research from operational to governance outcomes, providing a governance-level synergistic pathway to supply chain sustainability. 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 client ESG performance affects supplier total factor productivity (TFP) from a knowledge spillover perspective, using matched client–supplier–year data for Chinese A-share listed firms from 2010 to 2023. The results show that client ESG significantly improves supplier TFP; specifically, a one-unit increase in client ESG is associated with an average increase of approximately 8.3% in supplier TFP. These results remain robust across a series of robustness tests. Mechanism analysis indicates that client ESG enhances supplier productivity through three knowledge spillover channels: technical assistance, management sharing, and innovation induction. Heterogeneity analysis further shows that this positive effect is more pronounced in long-term cooperative relationships, among clients with stronger market power, for state-owned suppliers, and when clients and suppliers have aligned ownership structures. Further analysis shows that the positive effect of client ESG persists for at least three fiscal years and is more pronounced in industries characterized by lower volatility. These findings suggest that policymakers and firms should strengthen supply chain ESG governance to promote knowledge spillovers and improve productivity. 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

Peptide therapeutics offer high target selectivity and low toxicity, but their clinical utility remains constrained by rapid proteolysis in vivo and negligible oral bioavailability. Incorporating non-canonical amino acids (ncAAs) provides a robust molecular engineering framework to overcome these pharmacokinetic bottlenecks. This review analyzes the structural and biophysical design rules of ncAA-mediated peptide stabilization, categorizing them into side-chain steric shielding, backbone conformational constraint, and stereochemical evasion of L-specific proteases. We systematically evaluate the biosynthetic milestones enabling this field, focusing on engineered orthogonal translation systems (tRNA/synthetase pairs, orthogonal ribosomes, quadruplet codons) and metabolic engineering strategies that supply fluorinated and other ncAA precursors de novo. Furthermore, we examine the translation of these technologies into clinical candidates (e.g., modified antimicrobial peptides, antibody–drug conjugates, and PROTACs) and identify scaling, immunogenicity, and computational modeling as key bottlenecks. This review serves as a technical reference for designing next-generation, hyper-stable peptide therapeutics. 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

Background/Objectives: Cholera remains an important global health problem. Inactivated oral cholera vaccines (OCVs) are essential in the WHO/GTFCC (World Health Organization/Global Task Force on Cholera Control) strategy to end cholera by 2030; however, global supply is insufficient, they require partial cold-chain storage, and their formulation and antigen contents leave room for improvement. We describe here the development and preclinical evaluation of DuoChol OCV, a next-generation thermostable oral vaccine designed to address these gaps. Methods: DuoChol is a lyophilized dry-powder formulation in enteric capsules containing formalin-inactivated Vibrio cholerae O1 El Tor Ogawa and Inaba isogenic bacteria, recombinant cholera toxin B subunit (rCTB), and sucrose as stabilizer. Methods describe the construction of the novel vaccine strains, processes for the preparation and characterization of vaccine components, and the final dry formulation in enteric capsules, and in vitro and in vivo vaccine stability analyses. Results: The newly engineered vaccine strains, together with a high-yield mixed-mode chromatography process for rCTB purification, enabled efficient and cost-effective vaccine production. Stability studies demonstrated complete preservation of O1 LPS and rCTB antigens for at least 21 months across temperatures of 4–40 °C. Moreover, regardless of storage duration or temperature, oral immunization of mice with DuoChol elicited strong serum and mucosal antibacterial and antitoxin responses that were similar to those induced by the licensed Dukoral^® OCV. Conclusions: Its heat stability, practical enteric capsule formulation, and potential for improved efficacy compared to inactivated whole-cell only OCVs support positioning DuoChol as a promising next-generation OCV, suitable for national cholera control programs and particularly advantageous for outbreak response, where rapid deployment and early, robust protection are essential. 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

The selection of transportation service providers (TSPs) is a strategically critical decision in sustainable supply chain management. However, existing decision-making frameworks exhibit three recurring limitations: the absence of formally validated, sector-specific sustainability criteria; reliance on weighting methods that inadequately handle expert judgment uncertainty; and limited application to emerging market contexts, particularly export-oriented garment and textile industries facing growing environmental, social, and traceability pressures from global buyers. To address these gaps, this study develops and validates an integrated multi-criteria decision-making framework combining Content Validity Ratio $\left(CVR\right)$ analysis, the Fuzzy Best–Worst Method (FBWM), and Fuzzy Additive Ratio Assessment (FARAS). $CVR$ analysis was applied to an initial pool of $28$ candidate criteria, retaining $22$ validated criteria spanning economic, environmental, social, and operational dimensions. FBWM was subsequently used to derive criterion weights from nine decision-makers (DMs) representing garment manufacturers, transportation providers, and academia in Vietnam, while FARAS ranked five candidate TSPs. Results indicate that operational and economic criteria are the most influential dimensions, while cost for the service, financial performance, industry experience, environmental awareness, and environmental legal and policy framework emerge as the five highest-weighted sub-criteria. The final ranking order, TSP2 > TSP4 > TSP5 > TSP1 > TSP3, remained stable across benchmarking with FTOPSIS, FVIKOR, and FMOORA, as well as underweight perturbation and equal-weighting scenarios, confirming the robustness of the ranking results. 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