Search Results (3,922)

To analyze the effects of remanufacturing subsidies and emission reduction investments on pricing and quality decisions under cap-and-trade regulation, four profit-maximization Stackelberg game models for a remanufacturing supply chain (RSC), i.e., without remanufacturing subsidies and emission reduction investments, with remanufacturing subsidies only, with emission reduction investments only, and with both remanufacturing subsidies and emission reduction investments, are constructed, derived, compared, and analyzed. Results show that government subsidies and emission reduction investments can improve profits for the RSC members, while possibly leading to more total carbon emissions. Furthermore, it is worth noting that profit growth and emission reduction can be achieved even though reducing remanufacturing subsidies in some scenarios. Moreover, increasing emission reduction targets will reduce profits of the RSC members but does not necessarily contribute to emission reduction. Therefore, to help the RSC improve profits and reduce emission, the policymaker should formulate differentiated policies based on the types of manufacturers. For the non-abating manufacturer, the government should set higher emission reduction targets and cut down subsidies; for the low-efficiency abating manufacturer, higher emission reduction targets and subsidies are more suitable. However, for the high-efficiency abating manufacturer, lower emission reduction targets and subsidies are more effective. Full article

This paper presents a hybrid computational architecture for efficient and robust digital transmission inspired by helical genetic structures. The proposed system integrates advanced modulation schemes, such as multi-pulse-position modulation (MPPM), high-order quadrature amplitude modulation (QAM), and chirp spread spectrum (CSS), along with Reed–Solomon error correction and quantum-assisted search, to optimize performance in noisy and non-line-of-sight (NLOS) optical environments, including VLC channels modeled with log-normal fading. Through mathematical modeling and simulation, we demonstrate that the number of helical transmissions required for genome-scale data can be drastically reduced—up to 95% when using parallel strands and high-order modulation. The trade-off between redundancy, spectral efficiency, and error resilience is quantified across several configurations. Furthermore, we compare classical genetic algorithms and Grover’s quantum search algorithm, highlighting the potential of quantum computing in accelerating decision-making and data encoding. These results contribute to the field of operations research and supply chain communication by offering a scalable, energy-efficient framework for data transmission in distributed systems, such as logistics networks, smart sensing platforms, and industrial monitoring systems. The proposed architecture aligns with the goals of advanced computational modeling and optimization in engineering and operations management. Full article

This study explores the adoption of artificial intelligence (AI) in digital commerce platforms and whether such adoption is aligned with market positioning changes. Focusing on five of the largest e-commerce companies—Amazon, Apple, Shein, Temu, and IKEA—the study examines the application of AI in six key areas of operation: customer service, logistics, personalization, security, and supply chain management. A two-stage qualitative method was employed: a Scopus database-organized literature review, and a walkthrough examination of each company’s home page. There is extensive diversity in the deployment strategies of AI, which business models and digital maturity drive, the findings show. Amazon has end-to-end integration, but newer entrants such as Shein and Temu are concentrating on customer-facing AI tools. Apple, although it uses AI across its ecosystem, illustrates few examples in its online store. Notably, the rankings of firms under study align with their 2023 revenue rankings. Although no cause-and-effect relationship is assumed between the adoption of AI and revenue performance enhancement, the existence of a correlation suggests that AI could facilitate strategic differentiation. A comparative method for analyzing the adoption of AI is proposed in the study and highlights the importance of ethical, organizational, and regulatory concerns. Subsequent research should involve empirical measures of performance, longitudinal monitoring, and user-led assessments to enhance understanding of the impact of AI on digital trade. Full article

This review presents a critical analysis of Japan’s hydrogen strategy, focusing on the broader context of its decarbonization efforts. Japan aims to achieve carbon neutrality by 2050, with intermediate targets including 3 million tons of hydrogen use by 2030 and 20 million tons by 2050. Unlike countries with abundant domestic renewables, Japan’s approach emphasizes hydrogen imports and advanced storage technologies, driven by limited local renewable capacity. This review not only synthesizes policy and project-level developments but also critically evaluates Japan’s hydrogen roadmap by examining its alignment with global trends, technology maturity, and infrastructure scalability. The review integrates recent policy updates, infrastructure developments, and pilot project results, providing insights into value chain modeling, cost reduction strategies, and demand forecasting. Three policy conclusions emerge. First, Japan’s geography justifies an import-reliant pathway, but it heightens exposure to price, standards, and supply-chain risk; diversification across LH₂ and ammonia with robust certification and offtake mechanisms is essential. Second, near-term deployment is most credible in industrial feedstocks (steel, ammonia, methanol) and the maritime sector, while refueling rollout lags materially behind plan and should be recalibrated. Third, cost competitiveness hinges less on electrolyzer CAPEX than on electricity price, liquefaction, transport; policy should prioritize bankable offtake, grid-connected renewables and transmission, and targeted CAPEX support for import terminals, bunkering, and cracking. Japan’s experience offers a pathway in the global hydrogen transition, particularly for countries facing similar geographic and energy limitations. By analyzing both the progress and the limitations of Japan’s hydrogen roadmap, this study contributes to understanding diverse national strategies in the rapidly changing state of implementation of clean energy. Full article

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents—trained using centralized training with decentralized execution (CTDE)—handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems. Full article

This paper explores the integration of financial and operational flows in Supply Chain Finance (SCF) through the lens of Information Processing Theory (IPT). Despite increasing adoption of SCF solutions like reverse factoring and trade credit, existing literature lacks a unified theoretical framework that captures both financial and organizational complexities. Drawing from 47 peer-reviewed articles in leading supply chain journals, this study identifies key SCF dimensions—task characteristics, environment, and interdependence—as primary sources of uncertainty and information processing needs. It then examines how IT systems, coordination mechanisms, and organizational design enhance processing capacity, enabling firms to build SCF capabilities such as risk assessment, supplier onboarding, and financial process standardization. These capabilities facilitate financial supply chain integration through data connectivity, embedded flows, and collaborative planning. The study contributes a comprehensive conceptual model that connects SCF uncertainties, processing strategies, and performance outcomes, addressing theoretical and managerial gaps. It further provides a foundation for future empirical research and strategic design of SCF systems to enhance supply chain resilience and financial efficiency. Full article

With the progress of green transformation, government subsidies have become an important incentive for enterprises to invest in green technologies. However, their effectiveness differs markedly under alternative decision-making structures. This study develops a two-tier green supply chain game model comprising manufacturers and e-commerce platform self-operators. Six game structures are examined, covering both scenarios without subsidies and those in which manufacturers receive subsidies. The analysis focuses on product greenness, service levels, retail prices, and the profits of supply chain members. The results show that government subsidies substantially enhance manufacturers’ green investments and motivate platform self-operators to provide higher levels of green services, thereby improving market performance and overall supply chain profitability. Among the different structures, centralized decision-making demonstrates the strongest coordination effect and maximizes the subsidy impact. In contrast, within decentralized structures, subsidies help alleviate double marginalization, but their effectiveness is constrained by the distribution of power. These findings highlight the heterogeneous impacts of subsidies on green supply chain performance, offering theoretical support for targeted government policy design and practical guidance for enterprises to optimize green collaborative strategies. Full article

This study investigates the integration of Large Language Models (LLMs) into supply chain workflow automation, with a focus on their technical, operational, financial, and socio-technical implications. Building on Dynamic Capabilities Theory and Socio-Technical Systems Theory, the research explores how LLMs can enhance logistics operations, increase workflow efficiency, and support strategic agility within supply chain systems. Using two developed prototypes, the Q inventory management assistant and the nodeStream© workflow editor, the paper demonstrates the practical potential of GenAI-driven automation in streamlining complex supply chain activities. A detailed analysis of system architecture and data governance highlights critical implementation considerations, including model reliability, data preparation, and infrastructure integration. The financial feasibility of LLM-based solutions is assessed through cost analyses related to training, deployment, and maintenance. Furthermore, the study evaluates the human and organisational impacts of AI integration, identifying key challenges around workforce adaptation and responsible AI use. The paper culminates in a practical roadmap for deploying LLM technologies in logistics settings and offers strategic recommendations for future research and industry adoption. Full article

In increasingly complex Engineer-to-Order (EtO) supply chains, accurately predicting supplier delivery delays is essential for ensuring operational resilience. This study proposes an intelligent Internet of Things (IoT)-enhanced probabilistic framework for early warning and dynamic prediction of supplier lead times in smart manufacturing contexts. Within this framework, three novel Early Warning Systems (EWS) are introduced: the Baseline Probabilistic Alert System (BPAS) based on fixed thresholds, the Smart IoT-Calibrated Alert System (SIoT-CAS) leveraging IoT-driven calibration, and the Adaptive IoT-Driven Risk Alert System (AID-RAS) featuring real-time threshold adaptation. Supplier lead times are modeled using statistical distributions and dynamically adjusted with IoT data to capture evolving disruptions. A comprehensive Monte Carlo simulation was conducted across varying levels of lead time uncertainty (σ), alert sensitivity ($P_{threshold}$), and delivery constraints ($L_{max}$), generating over 1000 synthetic scenarios per configuration. The results highlight distinct trade-offs between predictive accuracy, sensitivity, and robustness: BPAS minimizes false alarms in stable environments, SIoT-CAS improves forecasting precision through IoT calibration, and AID-RAS maximizes detection capability and resilience under high-risk conditions. Overall, the findings advance theoretical understanding of adaptive, data-driven risk modeling in EtO supply chains and provide practical guidance for selecting appropriate EWS mechanisms based on operational priorities. Furthermore, they offer actionable insights for integrating predictive EWS into MES (Manufacturing Execution System) and digital control tower platforms, thereby contributing to both academic research and industrial best practices. Full article

Cross-border data flows are critical to the operation of global supply chains, particularly for digital enterprises such as Alibaba and Infosys. However, these flows introduce substantial challenges related to digital supply chain risk and cybersecurity management. This study examines how blockchain technology addresses these challenges within the operational contexts of Alibaba and Infosys. Unlike earlier research that often focused on sector-specific implementations or conceptual models, this study positions its findings within broader empirical evidence on blockchain-enabled supply chain governance, offering a comparative perspective that has been largely absent in prior work. Using an explanatory mixed-methods approach, the research combines thematic analysis of 85 peer-reviewed studies with in-depth case evaluations of the two firms. NVivo-based qualitative coding was applied to supporting sources, including GDPR audit reports, blockchain transaction records, and company disclosures. The findings demonstrate that blockchain adoption reduces cybersecurity breaches, enhances data integrity, and improves supply chain resilience. The study further shows how blockchain integration strengthens digital collaboration and regulatory alignment, enabling secure and uninterrupted data flows that support operational continuity and innovation. Overall, the research offers practical insights for digital enterprises and contributes to a deeper understanding of blockchain’s strategic role in cross-border data risk management. Full article

In the manufacturing supply chain, management reports often begin with concise messages that summarize key inventory insights. Traditionally, human analysts manually crafted these summary messages by sifting through complex data—a process that is both time-consuming and prone to inconsistency. In this research study, we present an AI-based system that automatically generates high-quality inventory insight summaries, referred to as “headline messages,” using real-world inventory data. The proposed system leverages lightweight natural language processing (NLP) and machine learning models to achieve accurate and efficient performance. Historical messages are first clustered using a sentence-translation MiniLM model that provides fast semantic embedding. This is used to derive key message categories and define structured input features for this purpose. Then, an explainable and low-complexity classifier trained to predict appropriate headline messages based on current inventory metrics using minimal computational resources. Through empirical experiments with real enterprise data, we demonstrate that this approach can reproduce expert-written headline messages with high accuracy while reducing report generation time from hours to minutes. This study makes three contributions. First, it introduces a lightweight approach that transforms inventory data into concise messages. Second, the proposed approach mitigates confusion by maintaining interpretability and fact-based control, and aligns wording with domain-specific terminology. Furthermore, it reports an industrial validation and deployment case study, demonstrating that the system can be integrated with enterprise data pipelines to generate large-scale weekly reports. These results demonstrate the application and technological innovation of combining small-scale language models with interpretable machine learning to provide insights. Full article

This simulation-based theoretical study addresses a critical gap by jointly assessing the technical performance and long-term economic sustainability of Solar Domestic Hot Water (SDHW) systems in economically volatile, import-dependent regions. Focusing on a fully operational system in a 700-bed dormitory at Middle East Technical University, Northern Cyprus Campus, TRNSYS 17 simulations were combined with a 15-year (2010–2024) cost trend analysis considering currency depreciation and construction price escalation. Results demonstrate that collector fluid temperatures exceeded 80 °C from April to October, maintaining domestic hot water above 60 °C for over seven months annually and reducing auxiliary heating demand by approximately 50%, translating into substantial annual energy savings. Economically, system component costs rose by 26–75 times, with circulation pumps showing the steepest increase (75×), highlighting vulnerabilities in import-dependent supply chains. Despite these cost escalations, the region’s high solar irradiation enables a competitive long-term investment profile, with potential payback periods remaining attractive under supportive policy frameworks. The originality of this work lies in its dual-focus methodology integrating performance modeling with economic resilience analysis, providing actionable insights for policymakers, designers, and investors in Mediterranean and similar climates seeking to balance renewable energy adoption with financial viability. Full article

Urban lifeline Natech events are coupled systems composed of multiple risks and entities with complex dynamic transmission chains. Predicting risk evolution probabilities is the core task for achieving the safety management of urban lifeline Natech events. First, the risk evolution mechanism is analyzed, where urban lifeline Natech events exhibit spatial evolution characteristics, which involves dissecting the parallel and synergistic effects of risk evolution in spatial dimensions. Next, based on fitting marginal probability distribution functions for natural hazard and urban lifeline risk evolution, a Multi-dimensional Copula (MdC) function for the joint probability distribution of urban lifeline Natech event risk evolution is constructed. Building upon the MdC function, a Markov Chain Monte Carlo (MCMC) model for predicting risk evolution probabilities of urban lifeline Natech events is developed using the Metropolis–Hastings (M-H) algorithm and Gibbs sampling. Finally, taking the 2021 Zhengzhou ‘7·20’ catastrophic rainstorm as a case study, joint probability distribution functions for risk evolution under Rainfall-Wind speed scenarios are fitted for traffic, electric, communication, water supply, and drainage systems (including different risk transmission chains). Numerical simulations of joint probability distributions for risk evolution are conducted, and visualizations of joint probability predictions for risk evolution are generated. Full article

Against the backdrop of the growing popularity of blockchain technology, this study investigates blockchain adoption strategies for the fresh food supply chain (FFSC) under a carbon cap-and-trade (CAT) policy. Taking a two-echelon supply chain consisting of a supplier and a retailer as an example, we designed four blockchain adoption modes based on the supplier’s strategy (adopt or not) and the retailer’s strategy (adopt or not). Combining influencing factors such as consumers’ low-carbon preference, consumers’ freshness preference, and carbon trading price (CTP), we established four game-theoretic models. Using backward induction, we derived the equilibrium strategies for the supplier and retailer under different modes and analyzed the impact of key factors on these equilibrium strategies. The analysis yielded four key findings: (1) BB mode (both adopt blockchain) is the optimal adoption strategy for both FFSC parties when carbon prices are high, and consumers exhibit strong dual preferences. It most effectively mitigates the negative price impact of rising carbon prices by synergistically enhancing emission reduction efforts and freshness preservation efforts, thereby increasing overall profits and achieving a Pareto improvement in the benefits for both parties. (2) Consumers’ low-carbon preference and freshness preference exhibit an interaction effect. These two preferences mutually reinforce each other’s incentive effect on FFSC efforts (emission reduction/freshness preservation). Blockchain’s information transparency makes these efforts more perceptible to consumers, forming a synergistic “emission reduction-freshness preservation” cycle that further drives sales and profit growth. (3) The adoption of blockchain by either the supplier or the retailer significantly lowers the cost threshold for the other party to adopt blockchain, thereby increasing their willingness to adopt. (4) CAT and consumer preferences jointly influence the adoption strategies of suppliers and retailers. Additionally, the adoption strategies of FFSC participants are also affected by the other party’s blockchain adoption status. Drawing on the above conclusions, this study provides actionable guidance for suppliers and retailers in selecting optimal blockchain adoption strategies. Full article

Accurate and non-destructive assessment of fruit firmness is critical for evaluating quality and ripeness, particularly in postharvest handling and supply chain management. This study presents the development of an image-based vibration analysis system for evaluating the firmness of kiwifruit using computer vision and machine learning. In the proposed setup, 120 kiwifruits were subjected to controlled excitation in the frequency range of 200–300 Hz using a vibration motor. A digital camera captured surface displacement over time (for 20 s), enabling the extraction of key dynamic features, namely, the damping coefficient (damping is a measure of a material’s ability to dissipate energy) and natural frequency (the first peak in the frequency spectrum), through image processing techniques. Results showed that firmer fruits exhibited higher natural frequencies and lower damping, while softer, more ripened fruits showed the opposite trend. These vibration-based features were then used as inputs to a feed-forward backpropagation neural network to predict fruit firmness. The neural network consisted of an input layer with two neurons (damping coefficient and natural frequency), a hidden layer with ten neurons, and an output layer representing firmness. The model demonstrated strong predictive performance, with a correlation coefficient (R²) of 0.9951 and a root mean square error (RMSE) of 0.0185, confirming its high accuracy. This study confirms the feasibility of using vibration-induced image data combined with machine learning for non-destructive firmness evaluation. The proposed method provides a reliable and efficient alternative to traditional firmness testing techniques and offers potential for real-time implementation in automated grading and quality control systems for kiwi and other fruit types. Full article