Search Results (172)

Background: Spare parts distribution networks (DNs) play a strategic role in retailers’ profitability. Among DN configuration decisions, selecting the optimal stock deployment policy—centralised, decentralised, or hybrid inventory allocation across distribution centres (DCs)—critically affects service levels and logistics costs. This decision becomes more complex with additive manufacturing (AM) as an alternative to conventional manufacturing (CM). While AM enables production with shorter lead times, its higher costs alter stock deployment cost-effectiveness. Given the complexity of joint stock deployment and manufacturing decisions, retailers require decision support systems (DSSs). Methods: To address this need, we develop a DSS through a three-step methodology: (i) a mathematical model evaluates logistics costs across different stock deployment policies and manufacturing technologies; (ii) parametric analysis tests the model across 2000 realistic scenarios; (iii) Random Forest trained on this dataset predicts optimal solutions, with SHapley Additive exPlanations (SHAP) interpreting post hoc recommendations. Results: The DSS achieves 93.4% prediction accuracy—outperforming (+16.4%) the only comparable literature DSS (77%)—while explaining recommendations. SHAP reveals that AM and CM unit costs dominate decision-making, followed by backorder costs. Conclusions: Beyond individual spare parts recommendations, the DSS provides guidelines enabling retailers to maintain cost-effective DNs aligned with evolving customer needs and to plan valuable investments in AM. Full article

The fashion industry’s business is becoming increasingly complicated and active. This industry is expected to be highly competitive, particularly in the retail sector. Therefore, this research aims to examine the impact of supply chain information sharing and quality management on firm performance, with supply chain agility as a mediating variable, in the Asian fashion industry. A total of 169 participants from the fashion sector in a developing country were surveyed. The proposed hypotheses were examined using a quantitative approach, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to assess and validate the measurement model. The results indicate that supply chain information sharing and quality management have a significant impact on a firm’s performance. Similarly, the sharing of supply chain information and quality management has a significant impact on firm performance by mediating supply chain agility. The study offers actionable insights for managers in volatile fashion supply chains. Firms can enhance performance by sharing real-time demand and inventory information, strengthening key quality practices, and adopting flexible, data-driven production processes. Integrating information sharing, quality management, and agility enables faster responses to shifting consumer trends, thereby improving overall competitiveness in fast-fashion environments. This study offers valuable guidance for supply chain professionals seeking to enhance practices within their networks. The results underscore the strategic importance of information sharing and quality management in promoting agility, an essential capability for achieving a competitive advantage. Additionally, the insights generated are relevant to practitioners, policymakers, and industry leaders aiming to strengthen supply chain responsiveness and resilience. Full article

China’s stringent clean air policies have substantially reduced nitrogen oxides (NO_x) emissions, leading to a general decline in nitrogen dioxide (NO₂). However, surface ozone (O₃) pollution remains severe, creating a complex challenge due to the non-linear relationship between O₃ and its precursors. To disentangle the drivers behind these trends, this study quantifies the impacts of interannual variations in top-down constrained NO_x emissions on surface NO₂ and O₃ concentrations from 2014 to 2021 across mainland China and five national urban agglomerations. We employed the WRF-CMAQ model with a fixed-emission simulation approach, using an observationally optimized NO_x emission inventory derived from the assimilation of surface NO₂ measurements. Results reveal that NO₂ reductions were predominantly emission-driven (>80% post-2017), with declines most pronounced in winter. A strong linear consistency was found between interannual changes in top-down NO_x emissions and attributed NO₂ concentration variations, validating the methodology. In contrast, O₃ responses to NO_x reductions were spatially and seasonally heterogeneous, reflecting a non-linear photochemical regime. In major urban agglomerations (e.g., Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD)), NO_x reductions post-2018 showed limited effectiveness in mitigating summertime O₃ and even increased O₃ in spring and autumn, indicating a prevalent VOC-sensitive regime where NO_x reduction can disinhibit O₃ formation. Conversely, certain provinces (e.g., Anhui, Shanxi, Jilin) exhibited O₃ decreases, suggesting a NO_x-sensitive regime. The area benefiting from NO_x reductions expanded steadily in summer after 2017 but not in other seasons. This study confirms the efficacy of NO_x-focused policies for reducing primary NO₂ pollution but highlights that mitigating persistent O₃ requires a strategic shift to synergistic, region-specific control of volatile organic compounds alongside NO_x, informed by local chemical sensitivity. Full article

Artificial intelligence (AI) has become a strategic resource for enhancing supply chain resilience in environments characterized by growing uncertainty and complexity. Building on the resource-based view (RBV) and organizational information processing theory (OIPT), this study examines how AI assimilation as a firm-level strategic capability improves supply–demand visibility and strengthens supply chain risk management (SCRM). Using survey data collected from 129 manufacturing firms in China, the proposed research framework is tested through structural equation modeling. The results show that AI assimilation significantly enhances both supply–demand visibility and SCRM, with visibility playing a partial mediating role in translating AI-enabled capabilities into more effective risk control. These findings indicate that AI contributes to resilience not merely through technological deployment but through its integration into organizational processes that support information processing and coordination. From a managerial perspective, the study suggests that firms should approach AI as an ongoing strategic capability development process rather than a one-time technological investment. By embedding AI into core supply chain functions such as production planning, inventory management, and demand forecasting, firms can improve visibility, anticipate disruptions, and shift toward more proactive and resilient risk management practices. This study advances the literature by integrating RBV and OIPT to explain the strategic mechanisms through which AI assimilation enhances visibility in SCRM, providing empirical evidence from a manufacturing context. Full article

This entry presents a conceptual approach for how artificial intelligence (AI) can be used to support high school and college students’ reading comprehension of complex texts across disciplines, using the Revised Metacognitive Awareness of Reading Strategies Inventory (MARSI-R), as an organizing framework. Drawing on research in literacy, learning sciences, and educational technology, the entry conceptualizes AI tools as potential metacognitive supports that can assist learners in planning, monitoring, and evaluating reading. At the same time, it distinguishes between AI use that risks promoting cognitive outsourcing, particularly when tools replace rather than support readers’ active regulation of meaning-making. The entry emphasizes the importance of instructional design and teacher mediation in aligning AI-supported reading practices with established models of metacognitive strategy use. Central to this discourse is the distinction between cognitive scaffolding, using AI to support and extend students’ strategic engagement within their zone of proximal development, and cognitive outsourcing, using AI to bypass cognitive effort entirely, thereby undermining active meaning-making. A distinctive feature of this entry is its use of MARSI-R not only as an assessment instrument but also as a design heuristic for structuring AI-supported reading interactions. By mapping AI affordances onto MARSI-R’s three strategy dimensions, the entry provides a conceptual bridge between established metacognitive theory and the practical design of AI-enhanced reading environments. This framing distinguishes the present contribution from prior work that treats AI tools and metacognitive frameworks as separate domains. Using MARSI-R’s dimensions of Global, Problem-Solving, and Support reading strategies, this entry describes how AI may provide personalized prompts and feedback that encourage strategic engagement with texts in STEM, the humanities, and social sciences. Illustrative classroom examples and research findings are used to highlight AI’s potential to support students in becoming “architects of their own understanding,” while also addressing ethical considerations such as overreliance on automated summaries and data privacy concerns. This entry offers a practical and theoretically grounded roadmap for integrating AI to support thoughtful, reflective reading across disciplines. Full article

Retailers must reevaluate store positioning when implementing omnichannel strategies. This study examines retailers’ strategic preferences toward in-store services, including Buy-Online-Pickup-in-Store (BOPS), Buy-Online-Return-in-Store (BORS), and their combined offering. A stylized model incorporating capacity constraints and strategic consumers’ purchase behavior is developed to analyze omnichannel impacts on brick-and-mortar operations from an inventory perspective. Firstly, profit-maximizing retailers benefit from reducing online channel inventory when handling product returns. Under high online return rates or stringent capacity constraints, retailers prefer maintaining physical-only channels to mitigate returns and capture cross-selling opportunities. Secondly, offering BOPS services remains a strategic advantage during periods of moderate capacity utilization. When the online consumer market expands, spillover effects increase, return rates decrease, and capacity constraints ease, it becomes feasible to consider jointly providing BORS services. Finally, BORS may migrate offline shoppers online, making it unsuitable for high-return-rate products. For omnichannel retailers, this study offers valuable insights into implementing omnichannel strategies under capacity constraints, empowering practitioners to make more informed decisions and optimize operational tactics. Full article

Drawing upon social presence and perceived value theories, this study examines how time-limited (TL) and quantity-limited (QL) promotions influence consumers’ purchase intention in live-streaming shopping. Through two controlled experiments (using countdown prompts for TL and inventory visualization for QL), the findings reveal distinct mechanisms: TL promotions elevate functional value by fostering a perception of collective synchronicity, whereas QL promotions boost social value identification through the perception of interactive control. Notably, this latter pathway is moderated by social cue sensitivity. Theoretically, this work unveils a “dual social presence–perceived value” framework that overcomes the limitations of single-mediation models and integrates evidence from eye-tracking and neurobehavioral analysis. Practically, it proposes a strategic promotion-matching criterion (recommending TL for high-circulation goods and QL for scarce items) to optimize live-streaming marketing effectiveness. Full article

To address collaborative decision-making challenges in the pharmaceutical logistics supply chain amid public health emergencies, this study integrates disappointment aversion, delay effects, and pharmaceutical value attenuation, constructing a three-echelon system. It adopts a “differential game-system dynamics (SD)” two-layer dynamic research method for in-depth synergy. The differential game model focuses on multi-agent dynamic strategic interactions, deriving time-series equilibrium solutions for the optimal effort levels, transportation efficiency, and profits under four decision modes (decentralized, government subsidy, cost-sharing, centralized) to clarify the dynamic impact laws of the core parameters. Compensating for its limitations in complex environmental coupling and practical variable integration, the SD model incorporates the patient consumption rate, inventory fluctuations, weather disturbances and other real factors to build a dynamic feedback system. It not only verifies the practical adaptability of the differential game equilibrium solutions but also reveals the evolutionary laws of supply chain performance and the amplified inter-mode performance differences under multi-factor superposition. This study finds that centralized decision-making performs the best in terms of transportation efficiency peaking, profit stability, and attenuation control. Pharmaceutical stability and enterprise effort levels positively drive benefits, while disappointment aversion and excessive delays exert inhibitory effects. Government subsidies need to be paired with collaborative mechanisms to avoid policy dependence. Management implications suggest that enterprises should prioritize the collaborative centralized-decision-making mode, establish risk-sharing and benefit-sharing mechanisms, precisely regulate key variables, and implement gradient subsidies with exit mechanisms to enhance the supply chain’s dynamic adaptability and achieve the triple optimization of “efficiency–profit–stability”. Full article

Many companies in the mining industry include decarbonization of production among their key strategic goals as part of their internal sustainability strategy. This need is driven by a number of factors: stricter regulation in the area of carbon footprint (introduction of carbon taxes, emissions quotas, reporting requirements); sustained growth in demand for electricity and rising market prices; economic feasibility—the need to optimize operating costs and improve energy efficiency. This study provides a comprehensive technical and economic justification for implementing a hybrid power supply system—combining a solar power plant (SPP) and a gas engine power plant (GPP)—at Solidcore Resources’ Varvarinsky hub in Kazakhstan. The methodology includes modeling the energy balance of the real asset (156.9 GWh of annual energy consumption), calculating the output of a 22.6 MW SPP based on local GHI/PR/η parameters, forming and determining the adaptability coefficient Kₐ (proportion of PV in total monthly electricity generation), conducting an economic assessment (NPV, payback period, sensitivity), and inventorying CO₂ emissions under Scope 1–2. The SPP provides approximately 41.3 GWh of electricity generation per year, with an average annual K_a = 0.263; the 40 MW installed capacity of the gas piston power plant covers the residual demand, forming a stable daily and seasonal balance. The project demonstrates a positive NPV (After Tax) = USD 23.65 million with an estimated payback period of 10 years, while the cost of energy in extraction and processing is reduced by almost three times, and the total reduction in CO₂ emissions will be 51%. Thus, hybridization of energy supply systems is a practical compromise between reliability and decarbonization. Determining the adaptability coefficient K_a allows the flexibility of the system to be taken into account, shows how effectively the new energy system uses renewable energy sources, and can be used to optimize the operation of the energy system to achieve the company’s internal sustainable development goals. Full article

The rapid growth of electric vehicles (EVs) has created an urgent need for sustainable end-of-life battery management systems. This paper presents a novel AI-driven framework for designing resilient reverse logistics networks that optimize the collection, testing, repurposing, and recycling of EV batteries within a circular economy context. We develop a bi-level optimization model in which the upper level determines strategic facility location decisions under disruption uncertainty, and the lower level employs deep reinforcement learning (DRL) to make dynamic operational decisions including battery routing, State-of-Health (SoH)-based sorting, and inventory management. The model simultaneously optimizes three objectives: total supply chain cost minimization, carbon emission reduction, and resilience maximization. A novel Fuzzy-Robust Stochastic programming approach with Conditional Value-at-Risk (FRS-CVaR) handles hybrid uncertainty from demand variability, supply disruptions, and material price volatility. We propose an enhanced Non-dominated Sorting Genetic Algorithm III (NSGA-III) integrated with Proximal Policy Optimization (PPO) for an efficient solution. The framework is validated through a comprehensive case study of the Gulf Cooperation Council (GCC) region, demonstrating that the AI-driven approach reduces total costs by 18.7%, decreases carbon emissions by 23.4%, and improves supply chain resilience by 31.2% compared to traditional optimization methods. Ablation studies across 10 independent runs with different random seeds confirm the robustness of these findings (95% confidence intervals within ±2.3% for all metrics). Sensitivity analysis reveals that battery SoH prediction accuracy and facility redundancy levels significantly impact network performance. This research contributes to both methodology and practice by providing decision-makers with an intelligent, adaptive tool for sustainable EV battery lifecycle management. Full article

This study investigates critical research gaps in procurement management challenges faced by Chinese contractors in international engineering–procurement–construction (EPC) projects under the Belt and Road Initiative (BRI), with a particular focus on sustainability-oriented outcomes. It examines the following: (1) prevalent procurement inefficiencies, such as communication delays and material shortages, encountered in international EPC projects; (2) the role of supply chain INTEGRATION in enhancing procurement performance; (3) the application of social network analysis (SNA) to reveal inter-organizational relationships in procurement systems; and (4) the influence of stakeholder collaboration on achieving efficient and sustainable procurement processes. The findings demonstrate that effective supply chain integration significantly improves procurement efficiency, reduces delays, and lowers costs, thereby contributing to more sustainable project delivery. Strong collaboration and transparent communication among key stakeholders—including contractors, suppliers, subcontractors, and designers—are shown to be essential for mitigating procurement risks and supporting resilient supply chain operations. SNA results highlight the critical roles of central stakeholders and their relational structures in optimizing resource allocation and enhancing risk management capabilities. Evidence from case studies further indicates that Chinese contractors increasingly adopt sustainability-oriented practices, such as just-in-time inventory management, strategic supplier relationship management, and digital procurement platforms, to reduce inefficiencies and environmental impacts. Overall, this study underscores that supply chain INTEGRATION, combined with robust stakeholder collaboration, is a key enabler of sustainable procurement and long-term competitiveness for Chinese contractors in the global EPC market. The purpose of this study is to identify critical procurement management challenges and propose evidence-based solutions for Chinese contractors. It further aims to develop a sustainability-oriented framework integrating supply chain integration and stakeholder collaboration to enhance competitiveness. Full article

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

As the global climate discourse shifts from mitigation to achieving net-negative emissions, there is a critical need for replicable, real-world models of climate-positive development at a regional scale, particularly in the Global South. This study addresses this gap by conducting a detailed greenhouse gas (GHG) inventory of four diverse provinces in Thailand and analyzing the results through the newly proposed Climate-Positive Pathways Framework (CPPF). Our findings reveal that all four provinces function as significant net-negative GHG sinks. They achieve this status through three distinct archetypes: a Conservation-Dependent pathway, an Agricultural Frontier pathway, and a novel Agro-Sink pathway. Most significantly, in the Agro-Sink model, we find that in specific economic contexts, managed agricultural landscapes can surpass natural forests as the primary driver of regional carbon removal. This typology provides a new, landscape-scale paradigm for cleaner production, proposing these three archetypes as transferable, evidence-based models for regional policymakers. This underscores that effective climate action requires context-specific regional planning that strategically leverages both natural and agricultural capital. Full article

In recent years, global supply chains have become increasingly vulnerable to geopolitical tensions, pandemics, and energy crises, particularly in resource-constrained regions characterized by weak infrastructure and high transaction costs. Using panel data on A-share listed firms in China’s central and western regions from 2010 to 2022, this study examines the effect of firm-level digital transformation on supply chain resilience. We construct a digital transformation index and employ an instrumental-variable approach based on the interaction between terrain ruggedness and lagged digital transformation to address endogeneity concerns. Empirical results show that the digital transformation of enterprises has significantly enhanced the resistance and recovery capabilities of the supply chain, verifying its effectiveness in resource-constrained environments. Mechanism analyses reveal that this effect operates through increased supply chain diversification—especially customer diversification—and improved supply–demand matching enabled by more accurate demand forecasting and inventory management. Heterogeneity tests indicate that the resilience-enhancing effects are more pronounced among non-state-owned firms, manufacturing enterprises, and firms in less technology-intensive industries. Overall, our findings provide empirical support for transaction cost economics, dynamic capability theory, and the resource-based view, highlighting the strategic role of digital investment in strengthening supply chain resilience in infrastructure-constrained settings and contributing to the aims of Sustainable Development Goal 9. Full article

Brazil possesses a large bioenergy resource, embedded in agro-industrial, livestock, and urban residues; this study quantifies its technical magnitude and associated energy value. An assessment was conducted by substrate, combining official statistics with literature-based yields and recovery factors. Biogas volumes were converted into biomethane using representative upgrading efficiencies, and thermal and electrical equivalents were derived from standard lower heating values and conversion efficiencies. Uncertainty bounds reflect the variability of feedstock yields and process performance. The national technical potential is estimated at roughly 80–85 billion Nm³/year of biogas, corresponding to ~43–45 billion Nm³/year of biomethane and around 168–174 TWh/year of electricity. Contributions are led by the sugar–energy complex (~one-third), followed by livestock and other agro-industrial residues (~one-third), while urban sanitation supplies ~8–10%. Potentials are concentrated in the Southeast, Center-West, and South, and current production represents only ~2–3% of the assessed potential. The findings indicate that realizing this potential requires targeted measure standardization for grid injection, support for pretreatment and co-digestion, access to credit, and alignment with instruments such as RenovaBio and “Metano Zero” to unlock significant methane-mitigation, air-quality, and decentralized energy-security benefits. Full article