Search Results (1,691)

Unexpected events have led to potential disruptions in upstream supply systems. Downstream enterprises often hold inventory to mitigate disruption risks. Previous studies have rarely focused on inventory under partial supply disruption, and responsive pricing and ordering decisions in two periods, even though this combined scenario is more applicable to real economic life. Motivated by the gap, this paper studies two-period game models of responsive pricing and ordering decisions without inventory (Model N) and with inventory (Model H), and considers partial supply disruption risks in the second period. We find that, under partial supply disruption, compared with the model without inventory (Model N), the order quantity of the model with inventory (Model H) may be either lower or higher; its retail price may also be either lower or higher. When the inventory holding cost is high, the retailer and supply chain are more inclined toward no inventory (Model N). Under certain conditions, the manufacturer supports the retailer in holding inventory. Interestingly, the expected profits of the retailer and the supply chain may increase as the disruption probability increases, and this holds true even when the inventory holding cost is very high. These findings have theoretical and practical significance for the resilience of supply chain systems. Full article

In make-to-order (MTO) manufacturing, customer-side issues involving payment, documentation, logistics, or site readiness may postpone shipment after order acceptance, increasing finished-goods inventory, capital tie-up, and capacity inefficiency. Although prior studies mainly address in-production delays or pre-order demand uncertainty, Customer-Induced Shipment Postponement (CISP) and its integration into production planning remain comparatively underexamined. This study proposes a predictive–prescriptive decision-support framework that uses ERP data from a real MTO manufacturer to predict shipment-timing outcomes associated with CISP and transfers the resulting predictions into an offline linear-programming-based production-planning model balancing capacity and inventory. Following CRISP-DM, 18,506 order records from 2017 to 2020 were analyzed, and eight machine-learning methods were evaluated across four scenarios combining Full and Parsimonious feature sets with three-class and binary target structures. Models were evaluated using an ex-post cost framework, with conventional classification metrics retained as diagnostic indicators. The selected model was interpreted using SHapley Additive exPlanations (SHAP). Under the stated evaluation assumptions, integrating predictions into the offline LP-based production plan reduced total planning cost by 22.85% relative to the no-prediction baseline. This suggests that the proposed framework can generate measurable planning value. Full article

Amid growing external uncertainty, strengthening supply chain resilience has become vital for the long-term survival and development of enterprises. Drawing on the Resource-Based View and Dynamic Capabilities Theory, this study develops a “capital–capability–resilience” research framework using panel data of China’s A-share listed firms from 2009 to 2024, and examines the influence of patient capital on corporate supply chain resilience. The results indicate that patient capital is positively correlated with enterprise supply chain resilience, and this conclusion remains stable after a series of endogeneity tests and robustness checks. Mechanism analysis demonstrates that patient capital can enhance supply chain resilience by upgrading firms’ dynamic capabilities, which specifically include absorptive capacity, innovation capacity and adaptive capacity. Heterogeneity analysis shows that the positive effect of patient capital on supply chain resilience is more significant among non-state-owned enterprises, firms facing high financing constraints, and enterprises in high-tech industries. Patient capital is identified as a distinctive strategic resource that underpins supply chain resilience; its three core attributes—long-term orientation, strong risk-bearing capacity, and strategic nature—differentiate it from traditional long-term capital forms. This study adds to the existing literature by clarifying the unique properties of patient capital and empirically verifying its function in building supply chain resilience, and further provides practicable managerial insights for cultivating patient capital and improving supply chain stability. Full article

Climate risk increasingly threatens agricultural supply chain stability. Green finance (GF) and environmental regulation (ER) are two key policy instruments intended to counter this risk, yet their effectiveness in fostering supply chain resilience (SCR) remains ambiguous. Using panel data from Chinese A-share-listed agricultural firms (2010–2024), this study examines the average and conditional associations of GF, ER, and climate risk awareness (CRA) with SCR via a two-way fixed-effects model, supplemented by instrumental variables, quantile regressions, heterogeneity tests, and interaction models. Text-based CRA is positively associated with SCR. Contemporaneous physical climate risk derived from meteorological observations shows no robust association, whereas its one-period-lagged, economic-scale-adjusted counterpart is significantly negative. Neither GF nor ER exhibits an unconditional linear relationship with SCR, and CRA substantially attenuates the marginal effect of GF. Financing constraints, supply chain concentration, factor allocation efficiency, and innovation intensity do not form stable indirect channels. Taken together, the findings suggest that the relationship between GF, ER, and agricultural SCR does not operate through a single transmission pathway, but may instead be shaped by whether firm-level risk sensing, resource mobilization, operational coordination, and structural reconfiguration form an effective capability configuration. Full article

Systematic risk plays a central role in company valuation, enterprise risk management, and sustainable investment. However, conventional approaches primarily rely on historical beta estimates and provide limited insight into the factors associated with future changes in systematic risk. This study develops an AI-supported framework for identifying the determinants of one-year-ahead industry beta coefficients for the US economy by combining macroeconomic variables with risk indicators derived from global news analytics. Annual industry betas published by Damodaran are transformed into monthly observations to align with lagged explanatory variables. The analysis combines macroeconomic indicators with twenty-two artificial intelligence-supported risk categories extracted from the GDELT database, collectively representing Dynamic Sustainability Risk. The empirical results show that historical beta persistence alone does not fully explain future industry beta coefficients. Sustainability-related factors—including ESG, supply-chain, technological, strategic, and labor-market risks—consistently appear among the significant determinants across industries, complementing traditional macroeconomic variables. Furthermore, forward-looking systematic risk is associated with interactions between macroeconomic conditions and dynamic sustainability-related risks rather than with historical financial information alone. Rather than developing a forecasting model, the proposed framework provides an interpretable approach for identifying the macroeconomic and sustainability-related determinants associated with future industry beta coefficients, thereby supporting company valuation, enterprise risk management, and sustainable financial decision-making. Full article

Background: The downstream oil and gas processing industry faces substantial environmental, regulatory, safety, and operational risks. Advanced technologies can reduce these risks and improve productivity, compliance, sustainability, and worker safety. However, despite adopting AI, analytics, machine learning, and IoT, Indian firms continue to lag behind their global counterparts, and Industry 5.0 adoption in this sector remains underexplored. Methods: This study examines Industry 5.0 adoption barriers using data from an Indian public-sector oil and gas organisation. Guided by the Resource-Based View and supply chain integration perspective, it applies a mixed-methods design combining a literature review, focus group discussion, Delphi analysis, and Fuzzy ISM–MICMAC. Forty-two barriers were identified and reduced to eight critical barriers. Results: Low technological maturity and lack of value-chain integration emerged as the principal driving barriers, influencing implementation failure, organisational technological readiness, and management commitment, which subsequently affect data quality. Geopolitics emerged as an autonomous barrier with both positive and adverse effects. Conclusions: This study develops a sector-specific framework explaining the hierarchical relationships among technological, organisational, sociotechnical, and value-chain barriers. It extends Industry 5.0 research in hazardous, human–technology-dependent operations and offers practical guidance for a human-centric, sustainable, and resilient transformation. Full article

As the U.S. entity-based control-list regime has evolved into a multidimensional restriction on technology access, collaboration, and financing, its implications for high-tech firms’ supply chain resilience remain insufficiently understood. Building on resource dependence theory and dynamic capabilities theory, this study aims to examine how the regime affects the supply chain resilience of Chinese high-tech firms, explore the underlying transmission mechanisms, and investigate the internal and external conditions under which its adverse effects can be mitigated. Using Chinese A-share listed high-tech firms from 2014 to 2024 and a staggered difference-in-differences design, we find that the regime weakens supply chain resilience. Mechanism tests show that reduced supply chain efficiency and financing constraints are two channels. Moderating analyses using pre-treatment interaction terms and triple-difference specifications show that this negative effect is attenuated by internal structural reconfiguration and capability activation, as reflected in lower supply chain concentration, greater digital transformation, and stronger continuous innovation, as well as by external resource support, as reflected in greater regional openness, stronger science and technology financial support, and a more favorable digital resource environment. The time-lag analysis further indicates that the adverse effect is more pronounced in the first post-shock year and becomes less evident in subsequent periods. Further analyses reveal industry peer effects and provide preliminary evidence that the supply chain resilience of potential domestic supplier firms may improve following the relevant downstream industry shock. This study advances supply chain risk management research by identifying how a specific geopolitical regulatory shock affects supply chain resilience and the conditions under which resilience losses can be mitigated. Full article

Point-of-care and near-patient diagnostics can shorten the time between testing and clinical or public-health action, but a technically effective test does not by itself create a sustainable diagnostic service. Evidence from diagnostic-access research, digitally connected point-of-care testing, human-centered design, and implementation science indicates that diagnostic technologies must be developed as components of wider service systems encompassing workflows, quality assurance, data governance, maintenance, supply chains, user trust, and linkage to care. Drawing on these bodies of literature, this Perspective introduces the I2Med digital infrastructure framework for point-of-care diagnostics in low-resource and infrastructure-constrained settings. These settings are defined functionally as clinical or public-health environments in which resource or infrastructure constraints can disrupt one or more stages between testing and appropriate action. The I2Med framework integrates six domains: (1) access and context of use; (2) the result-to-action diagnostic workflow; (3) digital interfaces and data governance; (4) quality, risk, usability, and design-control alignment; (5) sustainability, maintenance, and supply continuity; and (6) equity, trust, and community accountability. Operational tools—including a setting typology, a domain-to-artifact matrix, an evidence-to-scale maturity ladder, and a hypothetical application vignette—illustrate how the framework can guide development and implementation planning. The I2Med framework’s central contribution is to reposition the diagnostic device as one component of an interconnected service system and to provide academic, translational, and early-stage development teams with a common structure for identifying implementation dependencies, evidence gaps, and responsibilities. As a conceptual framework, it requires prospective application and evaluation across diverse settings before its utility and transferability can be established. Full article

Under the backdrop of the global economic transition towards high-quality development, green innovation has emerged as a pivotal driver in the pursuit of Sustainable Development Goals. However, enterprises often face financing constraints and high-risk challenges when engaging in green technology innovation. As a novel financial model that integrates financial resources with industrial operations, supply chain finance (SCF) has emerged as a potential solution. This study aims to investigate whether SCF effectively promotes corporate green innovation and to analyze its underlying mechanisms. Drawing on data from China’s A-share listed firms, this study measures corporate green innovation using patent data, which are identified from the IncoPat database based on the WIPO Green Inventory’s IPC classifications. Concurrently, firms’ deployment and depth of engagement in supply chain finance are assessed via textual analysis. The empirical findings demonstrate that SCF exerts a significant positive impact on fostering corporate green innovation. Mechanism analysis further uncovers that SCF enhances green innovation capabilities primarily through two pathways: mitigating financial constraints and accelerating the process of digital transformation. Results from the heterogeneity tests demonstrate that such an incentivizing impact is predominantly pronounced among enterprises characterized by superior ESG performance. Furthermore, our empirical investigation reveals a distinct inverted U-shaped pattern characterizing the nexus between SCF and corporate green innovation, which implies that an excessive dependence on SCF could ultimately impede innovative activities. We provide firm-level empirical evidence that deepens the understanding of SCF’s role in fostering green innovation, revealing that SCF is a key instrument for financial resources to integrate into the green transition. Policymakers and managers should utilize and develop SCF within a reasonable scale and focus on digital upgrading to achieve sustainable economic growth. Full article

Environmental, Social, and Governance (ESG) risks increasingly propagate across interconnected supply chains, yet conventional ESG assessment methods remain largely reliant on firm-level disclosures and static ESG ratings that often overlook indirect risk transmission among trading partners. This study develops a network-aware artificial intelligence (AI) framework for forecasting ESG risk by integrating Graph Neural Networks (GNNs), transformer-based natural language processing (NLP), explainable AI, and conventional machine-learning techniques. The proposed framework combines supply-chain network structures, shipment-level trade information, ESG controversy records, governance indicators, and transformer-derived ESG sentiment extracted using FinBERT and RoBERTa. Using a dataset of 11,386 firms across 27 industries from 2015 to 2025, the proposed GNN achieved the highest predictive performance, outperforming conventional machine-learning models with an ROC-AUC of 0.913. The results further demonstrate that supply-chain network centrality and transformer-derived ESG sentiment substantially improve the early identification of firms exposed to future ESG controversies. By integrating network relationships with textual ESG intelligence, the proposed framework advances FinTech-enabled ESG analytics and provides a scalable approach for proactive risk monitoring, sustainable investment decision-making, and supply-chain risk management. Full article

In the context of economic globalization, effective cooperation and information sharing are critical for enhancing the efficiency and competitiveness of supply chains. Fresh agricultural product supply chains face distinctive challenges arising from high perishability, rapid demand fluctuations, and information asymmetry among supply chain members. This study develops an analytical framework comprising the following three conceptually connected but independently calibrated modules: an EOQ cost module, a comparative profit module, and a two-period, two-task dynamic principal–agent module. The modules are connected through the common economic logic of value creation, value distribution, and incentive design, rather than through a one-to-one numerical mapping. Using operational and financial information from the Erli River Crab supply chain in Panshan County, the EOQ and comparative profit models are independently calibrated at different decision scales to evaluate the benchmark economic effects of information-sharing-enabled coordination. The EOQ analysis uses annual aggregate operational quantities, whereas the comparative profit analysis uses a normalized transaction-demand scale; therefore, their numerical quantity magnitudes are not intended for direct one-to-one comparison. Within the EOQ module, information-sharing-enabled coordination reduces total relevant supply chain cost by 8.41% relative to the farmer-led decentralized benchmark and by 60.75% relative to the retailer-led decentralized benchmark. The difference arises because the farmer-led batch quantity is closer to the coordinated optimum, whereas the retailer-led order quantity is substantially smaller, implying a higher modeled frequency of upstream setup activities and a larger setup-cost component under the benchmark parameterization. These results capture the joint effect of full information availability and coordinated batch optimization under two alternative decentralized decision regimes. Within the comparative profit module, over the benchmark wholesale-price interval, the coordinated full-information-sharing benchmark increases total supply chain profit by 1.38–2.30% relative to the decentralized no-information-sharing benchmark. Under the unchanged-wholesale-price comparison, the farmer’s profit increases by 13.21–17.65%, whereas the retailer’s profit decreases by 1.74–3.11%. These results identify positive aggregate value creation and an asymmetric initial allocation under the linear-demand and unchanged-wholesale-price benchmark. This asymmetric allocation is benchmark-specific rather than an unavoidable consequence of information-sharing-enabled coordination, because a negotiated transfer or wholesale-price adjustment can redistribute the additional surplus. The extended analysis derives a participation-compatible transfer interval within which both the farmer and the retailer are weakly better off than under decentralized decision-making. Within the dynamic principal–agent module, a case-motivated illustrative simulation using standardized benchmark parameters shows that more informative intertemporal performance signals strengthen the optimal second-period incentive coefficient, whereas greater risk exposure limits the appropriate intensity of performance-based compensation. Continuation–payoff and ratchet-like effects jointly shape first-period information-sharing effort. Together, the three modules show how information-sharing-enabled coordination affects operational efficiency, surplus allocation, and intertemporal incentives. Full article

European energy markets have experienced significant instability as a result of consecutive global systemic shocks, particularly the COVID-19 pandemic and the geopolitical conflict in Ukraine. This paper analyses the development of crude oil and natural gas prices between 2019 and 2024 and discusses their implications for sustainable logistics, energy security and supply chain resilience in Europe. The study is based on secondary data from internationally recognized sources, including the International Energy Agency, OPEC, Eurostat, the European Council, the World Bank and the U.S. Energy Information Administration. An event-based comparative approach supported by descriptive price-change calculations was applied to distinguish between the pandemic-related demand shock and the geopolitical supply-side shock after 2022. The results show that crude oil prices declined from approximately 64 USD/barrel in 2019 to 41 USD/barrel in 2020, representing a decrease of about 3f5.9%, mainly in connection with reduced mobility, lower transport activity and industrial slowdown during the COVID-19 pandemic. In contrast, crude oil prices increased to approximately 100 USD/barrel in 2022, representing an increase of about 143.9% compared to 2020, coinciding with geopolitical uncertainty and supply-side pressures. The European natural gas market appeared particularly vulnerable to the 2022 crisis because of supplier dependence, pipeline infrastructure constraints and reduced Russian gas flows. EU natural gas demand declined by 55 billion m³, or 13%, in 2022, indicating the effect of high prices, energy savings and crisis adaptation. The findings suggest that crude oil shocks are mainly related to transport costs and freight rates, while natural gas shocks may influence energy-intensive production, warehousing, cold chains and broader supply chain stability. The study highlights the need for energy diversification, renewable and low-carbon energy development, energy efficiency and more resilient logistics strategies. Full article

The transportation sector faces growing supply chain disruptions driven by workforce shortages, digital skills gaps, and weak cultural readiness for risk. Human Resource Management (HRM) offers a credible path toward stronger supply chain resilience (SCR), yet existing decision support tools lack the formal reasoning and auditability that systematic HR risk assessment requires. This paper proposes a Task-Typed Reasoning Module-Enhanced Retrieval-Augmented Generation (RAG) Expert System framework for HR-driven risk assessment in transportation supply chains. The framework employs a six-layer architecture integrating four core components: a Task-Aware RAG layer for knowledge extraction from heterogeneous HR documents, a Task-Routed Evidence Extractor for risk factor identification and source reliability scoring, a Task-Based Reasoning Core applying weighted Mamdani fuzzy inference, and a Task-Guided Synthesis Module using Dempster–Shafer evidential reasoning for risk profiling. Task-Typed Reasoning Modules (TTRMs) organise reasoning into reusable, adaptable units covering Likelihood, Impact, Vulnerability, Mitigation, and Prioritisation, refined through an expert-gated feedback loop. Applied to an illustrative UK transportation logistics case study, the framework ranked HR-driven workforce risks, quantified uncertainty through belief-plausibility intervals, and generated traceable recommendations linked to four HRM-SCR dimensions. As a proof-of-concept demonstration, the framework provides a conceptual foundation for HR risk management in transportation, extending expert system reasoning through adaptive AI to offer mathematically grounded, traceable outputs for practitioners and researchers; empirical validation in live organisational settings remains a necessary next step. By formalising the human dimension of resilience, the framework contributes toward socially, economically, and environmentally sustainable transportation supply chains, aligning HR risk management with the sustainable development objectives examined in this study. Full article

Demand uncertainty and supply imbalances pose persistent challenges for procurement decisions. In practice, firms often mitigate such mismatches through inventory swapping and spot market procurement. Motivated by this observation, this paper develops a procurement model that integrates long-term wholesale procurement, ex-post inventory swapping, and spot-market sourcing within a unified framework. While prior studies have largely examined spot-market procurement and inventory swapping separately, little attention has been paid to how these two mechanisms jointly mitigate demand uncertainty. The analysis yields three main findings. First, inventory swapping remains economically valuable even when spot-market procurement is available, indicating that the two mechanisms are not pure substitutes. Second, the economic value of inventory swapping increases with higher spot-market prices and greater demand volatility, as reallocating existing inventory becomes more cost-effective than emergency spot-market procurement. Third, the impact of the partner buyer’s order quantity depends critically on the swap transfer price, with lower transfer prices strengthening the value of inventory swapping. These findings contribute to the procurement and supply flexibility literature by clarifying how multiple procurement channels interact under demand uncertainty. The study also provides managerial implications for designing procurement systems that integrate inventory swapping with spot-market procurement. Full article

Spice contamination and adulteration are longstanding global concerns across multiple points of the food supply chain, from production to distribution. Dietary supplements intended for pregnant and lactating individuals have also been found to contain heavy metals at levels exceeding regulatory recommendations. This review examines spices and dietary supplements as underrecognized, non-traditional sources of heavy metal exposure, with an emphasis on maternal and infant health. Published studies on lead contamination in spices and supplements were evaluated alongside evidence related to pregnancy, lactation, infancy, and early childhood. Relevant U.S. and international regulatory frameworks were also reviewed to identify policy and practice gaps that may limit prevention efforts. Findings highlight an important connection between food safety, regulatory oversight, and maternal and infant health. Lead contamination in spices and dietary supplements represents a preventable yet underrecognized public health risk that underscores the need to address regulatory gaps. International, federal, and state-level actions across the supply chain may help reduce heavy metal contamination in food sources. Further exploration of exposure pathways and policy responses is needed to strengthen standards, improve supply-chain transparency, and enhance product reliability while protecting maternal and infant health. Full article