Search Results (1,189)

The construction sector must simultaneously meet rising global demand and cut embodied carbon deeply enough to satisfy European Green Deal and Bioeconomy Strategy targets—two pressures that conventional petrochemical-derived materials are poorly placed to resolve. Bio-based alternatives offer a credible path: they sequester carbon, carry lower embodied emissions, improve indoor air quality, and fit naturally within circular economy models. Yet they remain marginal in specification practice. This paper reviews the evidence on bio-based construction materials and maps the barriers that keep them there. The analysis organises these barriers into four levels—structural, economic, technical, and enabling—and traces the conditional relationships between them, with direct consequences for how policy interventions should be sequenced. The strategic case for this transition extends beyond environmental policy: the 2026 Strait of Hormuz disruption is used here as a scenario to show how dependent European construction is on fossil-derived material inputs, and how exposed that dependence leaves the sector to geopolitical supply shocks. The principal obstacles to adoption prove to be institutional and economic rather than technical—regulatory fragmentation, absent harmonised standards, fragile supply chains, and market structures that systematically undervalue bio-based solutions. The paper concludes that meaningful scaling requires coordinated action across governance, market design, and industrial policy, and that material and performance advances alone will not deliver it. Full article

Increasing external uncertainty, supply disruptions, and market volatility have made resilience enhancement increasingly important for sustainable agricultural supply chains. While existing studies mainly examine agricultural supply chain resilience from macro or operational perspectives, limited attention has been paid to how firms’ strategic AI investment reshapes organizational resilience under external shocks. Using panel data on Chinese agricultural-related listed firms from 2010 to 2024, this study examines whether and how strategic AI investment enhances supply chain resilience. Empirical results show that strategic AI investment significantly improves both dimensions of supply chain resilience, namely resistance capacity and recovery capacity. Mechanism analyses indicate that this effect mainly operates through supply diversification, technological innovation, and information transparency. Further analyses reveal heterogeneous effects across supply chain positions, ownership structures, and regional digital development environments. In addition, compatibility analyses show that strategic AI investment not only strengthens supply chain resilience but also improves operational efficiency, R&D investment intensity, and financial stability. Overall, this study highlights strategic AI investment as an important organizational capability for strengthening agricultural supply chain resilience under increasing external uncertainty. Full article

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations. Full article

Non-timber forest products (NTFPs) constitute an important component of forest-based production systems and biomass supply chains in Türkiye. Despite their growing economic and ecological significance, the long-term structural dynamics of NTFP production remain insufficiently understood. This study examines temporal and structural changes in NTFP production in Türkiye during the period 1988–2024 using official production statistics and production support data. The analysis applies a quantitative framework that combines linear trend analysis, Shannon diversity and Herfindahl–Hirschman concentration indices, volatility measures based on the coefficient of variation, and regression models to evaluate production trends, structural transformations, stabilization patterns, and the effectiveness of production support mechanisms. The findings reveal a non-linear and multi-phase development pattern characterized by diversification and production growth after 2000, followed by increasing concentration and greater production volatility after 2018. Although total production volume increased substantially, portfolio diversity declined over time, and dependence on a limited number of high-volume products intensified, indicating growing structural vulnerability within the system. In addition, production support mechanisms showed a weak and heterogeneous relationship with production outcomes. A limited contextual comparison with Lithuania’s multifunctional NTFP system is also included to position the findings within a broader European context. Overall, the results suggest that increasing production alone is insufficient to ensure long-term system stability. Instead, diversification-oriented and risk-sensitive resource management strategies that account for production risks, regional disparities, and product heterogeneity are essential for developing sustainable and resilient NTFP production systems. Full article

Background: Reconfigurable Manufacturing Systems (RMSs) address the deficiencies of previous manufacturing systems with expandable capacity and capability to respond to dynamic demand. While research on RMSs has been ongoing for decades, comprehensive classifications and categorizations of RMS research and their supply chain implications are sparse. Methods: The PRISMA 2020 guidelines were used for a systematic literature review on the Scopus database covering peer-reviewed publications from 1990 to 2025, and 247 papers were analyzed based on a four-stream classification framework (research scope, industry sectors, type of research, and RMS characteristics) that was inductively derived. Furthermore, a three-level conceptual model connecting RMS characteristics with manufacturing capabilities and supply chain resilience was established. Results: RMS research, particularly post 2020, has seen significant growth. However, RMSs are mainly oriented to heavy industries, while process industries and supply chain implications have been understudied. The dominance of theoretical research over experimental/practical research points to a theory-practice gap. Modularity is the most frequent RMS characteristic, underpinning the others, while diagnosability, despite its operational importance, is the least studied one. Conclusions: RMSs have significant potential as a supply chain resilience enabler through their characteristics. Nevertheless, this relationship is mostly theoretical and untested in practice, requiring interdisciplinary and application-oriented research. Full article

Utility disruptions may stem from insufficient power generation, inferior infrastructure, or secondary weather perils (e.g., tornadoes, floods, snowstorms) that take energy infrastructure offline. The latter present a unique risk that not all existing power options can mitigate. Regardless of their origin, power disruptions have the potential to cripple food supply chains and undermine food system sustainability. To prepare for managing future disruptions, food and beverage manufacturers may couple electrical microgrid and thermal district heating infrastructure with small modular reactors (SMRs) or smaller microreactor systems to form low-carbon power islands. Although SMR technology is a somewhat new source of energy and has not yet achieved commercial viability, it provides the potential to make food and beverage manufacturing more resilient and sustainable when it becomes broadly available. To assess the potential cost–benefit of activating such technology as a sustainability-oriented resilience investment, we conducted a technoeconomic downtime threshold analysis. The case assumes that the technology is the full-time power source and the SMR yields stronger returns as facility downtime or downtime costs rise. The analysis found the breakeven point to range from 12.3 h down to 613.2 h down annually for a 5 MW system, depending on facility scale and assumed downtime costs. At a representative downtime opportunity cost of $10,000/h, SMR adoption requires approximately 61.3 h (5 MW) of annual outages to break even, highlighting scale effects on feasibility. Incorporating a 20% thermal energy credit reduces required outage thresholds by roughly 20%, lowering the breakeven level to 49.1 h. These results highlight the potential role of SMR-enabled power islanding in supporting sustainable food manufacturing through improved energy resilience, low-carbon power, and thermal energy recovery. Full article

Disruptions threaten supply chains, creating a need for more resilient manufacturing networks. Manufacturing-as-a-Service (MaaS) has emerged as a promising Industry 4.0 approach to address this challenge. Yet, its effectiveness relies on interoperable digital twins (DTs), enabling the standardized exchange of manufacturing capabilities across organizational boundaries. The Asset Administration Shell (AAS) standards can be used to meet this requirement. However, modeling AAS-compliant DTs is considered challenging due to the standard’s complexity. This paper, therefore, investigates the automatic generation of AAS-compliant DTs for representing manufacturing capabilities. Requirements from MaaS use cases in two research projects reveal limitations in current approaches. To address these limitations, this paper introduces an automated, LLM-supported generation process that leverages ontologies as a domain-specific knowledge base. The approach is operationalized in a modular software architecture and demonstrated through two use cases. Full article

Tungsten is a critical strategic resource whose supply chain has become increasingly exposed to external trade shocks, raising concerns about its resilience and sustainability. However, existing studies mainly focus on single products and lack a systematic analysis of multi-stage supply chain networks under trade shocks. Using trade data for 66 countries from 2012 to 2023 obtained from the UN Comtrade database, this study constructs a multi-stage trade network of the global tungsten supply chain, covering upstream, midstream, and downstream segments, and combines complex network analysis with a difference-in-differences (DID) approach to examine whether and how Sino–US trade friction affects supply chain resilience. The results show that the trade network exhibits significant structural heterogeneity across segments, with downstream networks being more complex and interconnected; trade friction has no significant effect on upstream and midstream segments but has a significant positive effect on downstream network centrality, indicating stronger adaptability and structural resilience in downstream segments; the results further suggest that the observed downstream adjustment is mainly associated with changes in China’s network position, while the impact on the United States remains statistically insignificant. This study contributes to the literature by integrating network analysis with causal inference in a supply chain framework and provides new evidence on the heterogeneous effects of trade shocks across different stages of strategic resource supply chains under geopolitical risks. Full article

By allowing continuous connectivity, automation, and data-driven decision-making across these areas, Internet of Things (IoT) has transformed certain facets of daily life, including home automation and healthcare, as well as business operations like supply chain management and smart manufacturing. IoT systems are susceptible to different cyberattacks, though, because of different designs, lack of funds, and inadequate security policies, which creates major security issues given their fast growth. Covering important topics including protocols, architectures, attack classification, detection methods, countermeasures, and research issues, this paper offers a thorough study of IoT security. Emphasizing their relevance in enhancing the security of IoTs, the article offers a thorough analysis of machine and deep learning-based detection techniques. It also offers recommendations for future paths to handle changing risks by means of particular proposals and provides tools and datasets required for IoT security studies. When considering recent progress, however, there are still some major limitations in scaling, real-time detection, dataset availability, and versatility of current solutions. We identified these issues and provided guidance on future research; we also offered a selected set of tools and datasets for further research. Additionally, this paper provides an overview of the most important issues related to IoT security as documented in the current literature, providing a framework for developing resilient and adaptable IoT security solutions in the future. Full article

Late deliveries represent one of the most persistent operational disruptions in global supply chains, eroding service reliability, triggering contractual penalties, and undermining the resilience of logistics networks. As supply chains become increasingly digitalized, the integration of advanced predictive analytics into operational decision-making offers a pathway toward proactive rather than reactive disruption management. This study develops and evaluates a digital analytics framework in which eXtreme Gradient Boosting (XGB), a high-performance ensemble learning algorithm, is optimized by three recent population-based metaheuristic algorithms: the weighted mean of vectors algorithm (INFO), Harris Hawks Optimization (HHO), and the Red-Billed Blue Magpie Optimizer (RBMO). Four critical XGB hyperparameters, number of estimators, maximum tree depth, learning rate, and complexity penalty, are tuned on a supply chain dataset. A population-size sensitivity analysis at two swarm configurations reveals that all three optimizers converge to functionally equivalent solutions at sufficient population diversity, providing practical guidance for computational resource allocation. The best-performing configuration, HHO-XGB, achieves a test accuracy of 97.47% and a Matthews correlation coefficient of 0.949, substantially outperforming the baseline XGB and other benchmark classifiers. To ensure transparency and support data-driven decision-making, SHapley Additive exPlanations (SHAP) analysis is applied to the optimized model, revealing that shipping mode, scheduled shipment days, shipping date, order day, order status, and order month are the dominant predictive features, confirming that late-delivery risk is primarily driven by shipment configuration and temporal patterns. The proposed framework demonstrates that integrating metaheuristic intelligence with machine learning delivers better predictive performance. Interpretability is essential to trustworthy, resilient supply chain decision-support systems. Full article

Global climate warming has led to the frequent occurrence of natural disasters, threatening the stability of agricultural production and the survival of agricultural enterprises. The existing literature presents mixed evidence regarding the impact of natural disasters on corporate ESG performance. Some studies argue that natural disasters promote ESG performance; however, such conclusions only hold for non-agricultural enterprises. Agricultural enterprises are highly dependent on natural conditions, and their core production factors are vulnerable to direct damage from natural disasters. Meanwhile, they are characterized by long production cycles and high asset specificity. After disaster shocks, agricultural enterprises have to prioritize production recovery, so natural disasters exert a dominant negative effect on their ESG performance. Based on the above context, here we take the performance of Chinese A-share listed agricultural companies between 2010 and 2023 as the research sample to explore the impact of natural disasters on the ESG performance of agricultural enterprises. The empirical results show that natural disasters significantly inhibit the ESG performance of agricultural enterprises. Mechanism tests indicate that natural disasters weaken ESG performance by damaging supply chain resilience, hindering green innovation, and disrupting internal control. A cross-sectional heterogeneity analysis reveals that the inhibitory effect is more pronounced for large-scale enterprises, enterprises with lower executive green cognition, and enterprises located in areas that are not major grain-selling areas. This study enriches the research on the economic consequences of natural disasters and the factors influencing corporate ESG performance. It also provides important practical implications for strengthening the ESG fulfillment of agricultural enterprises and accelerating the cultivation of new productive forces in agriculture. Full article

The decoupling of physical loading configurations from dynamic temperature control in cold-chain logistics exposes supply chains to severe thermal compliance risks and exponential cost penalties. To address this structural gap, this study formulated the Cold Chain Unitization Loading Optimization Problem (CCULP). We propose a mixed-integer linear programming (MILP) model that integrates continuous-time heat-transfer dynamics—including door-opening impulse disturbances—and Q10-driven quality-decay kinetics as endogenous constraints within the hierarchical assignment of perishable goods to insulated containers, pallets, and vehicles. By treating container thermal resistance as a core decision variable, the model operationalizes a “prevention-first” economic strategy. To solve this NP-hard problem, we developed a Temperature-Aware Heuristic Algorithm (TAHA) that embeds a forward-Euler temperature simulation loop directly into the combinatorial search. Computational experiments on instances up to 100 SKU types demonstrate that TAHA achieves near-optimal solutions (within 0.7% of the MILP proven optimum) while converging 63 times faster than a genetic algorithm benchmark. Moreover, compared with traditional geometry-centric heuristics, TAHA’s proactive container-polarization strategy effectively eliminates the “penalty cliff,” yielding up to a 25.9% reduction in total system cost on Large-scale instances, almost entirely attributable to the elimination of temperature-violation penalties. Sensitivity analyses further confirm TAHA’s robustness under extreme environmental stress (e.g., 40 °C ambient temperatures) and frequent logistical disturbances, offering an integrated framework for proactive risk mitigation and for reducing food loss in sustainable temperature-controlled distribution. Full article

Supply chain conditions can influence firm risk by shaping operational stability and dependence within customer and supplier relationships. This study examines whether ESG performance mitigates corporate risk in such settings. Using panel data on publicly listed firms, the paper shows that higher ESG scores are associated with lower total volatility, lower idiosyncratic volatility, and lower stock price crash risk. The paper further shows that this risk-reducing effect is more pronounced when firms face greater supply chain dependence or operational instability, as captured by trade credit dependence measures and inventory-based measures. Decomposing ESG into its environmental, social, and governance dimensions, the results show that the stronger risk-mitigating effect of ESG is driven primarily by the governance dimension, with some additional support for the social dimension and relatively weak evidence for the environmental dimension. These findings suggest that ESG is especially valuable in supply chain environments where dependence, instability, and operational frictions are greater. Overall, the results indicate that ESG performance and supply chain conditions jointly shape corporate risk and resilience. Full article

The integration of Artificial Intelligence (AI) and Big Data is increasingly associated with firms’ resilience in dynamic business environments. This study examines the relationships between AI–Big Data integration, AI capabilities, supply chain agility, and firm resilience, with particular attention paid to the mediating roles of AI capabilities and supply chain agility. Data were collected from 475 experts across firms in Saudi Arabia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that AI–Big Data integration is positively associated with AI capabilities and supply chain agility, both of which, in turn, significantly contribute to firm resilience. In addition, AI capabilities show a direct positive relationship with supply chain agility. The findings further confirm the mediating roles of AI capabilities and supply chain agility in strengthening organizational resilience. This study contributes to the Dynamic Capabilities View (DCV) and Knowledge-Based View (KBV) by empirically examining how integrated AI–Big Data relates to capability development and firm outcomes. The results also provide implications for managers seeking to align AI and Big Data initiatives with supply chain capabilities to support resilience in dynamic environments. Full article

This narrative review summarizes evidence on the bovine β-casein (CSN2) A1/A2 polymorphism as a case study of how intensive dairy selection and global gene flow can reshape allele frequencies in ways that matter for consumers, processing and agroecological resilience. We draw together evidence from (i) population-genetic surveys of CSN2 in contrasting cattle populations, including a descriptive summary of published genotype-frequency studies; (ii) controlled human studies that separate A1-containing from A2-only dairy exposure; and (iii) dairy technology and the authenticity literature relevant to identity-preserved A2 value chains. Across intensively selected Holstein-Friesian populations, A1 was consistently present at substantial frequency (approximately one-third), whereas indigenous, beef and zebu-adjacent populations were typically A2-enriched, highlighting the role of historical breed formation and modern introgression in shaping apparent geographic and climatic patterns. Human intervention studies most consistently support improved short-term gastrointestinal tolerance with A2-only milk in susceptible individuals, while evidence for longer-horizon systemic outcomes remains mixed and insufficient for causal disease claims. Processing and analytical studies suggest that β-casein genotype can modestly affect coagulation and product behavior in a context-dependent manner and that validated proteoform quantification coupled with traceability is essential for credible A2 labeling at scale. We discuss implications for breeding programs, including staged A2 selection that avoids performance trade-offs, and emphasize governance of artificial insemination and supply-chain segregation as levers to limit inadvertent allele diffusion while supporting climate-relevant genetic resources in locally adapted breeds. Collectively, the reviewed evidence suggests that A1/A2 β-casein can be usefully interpreted within a One Health framework spanning animal genetics, dairy systems and human tolerance research. Full article