技术链

In the contemporary business landscape, effective supply chain management (SCM) is paramount for organizations seeking to thrive amidst evolving market dynamics and heightened customer expectations. This research paper presents a pioneering approach to SCM that harnesses cutting-edge technologies, namely Kafka and Akka, to revolutionize data integration and decision-making processes. By leveraging Kafka as a robust distributed event streaming platform and Akka as a versatile toolkit for developing concurrent and distributed applications, our system facilitates seamless communication and coordination across diverse nodes within the supply chain network. This paper elucidates the intricacies of the proposed architecture, detailing the implementation methodology and performance evaluation metrics. Through a comprehensive examination, we demonstrate how our solution enhances supply chain visibility, fosters operational agility, and enables real-time responsiveness to market fluctuations and customer demands. Moreover, practical use cases exemplify the transformative impact of our approach on inventory management optimization, order fulfillment efficiency, and logistics optimization. Furthermore, we delve into the challenges encountered during implementation and deployment, offering insights into potential mitigative strategies. Finally, we outline avenues for future research, exploring emerging trends and opportunities in the realm of SCM empowered by Kafka and Akka technologies.

Background: This study investigates digital transformation as a moderating variable in determining the effect of digital technologies, automation, and data integration of upstream and downstream providers on supply chain performance. By filling the existing research gap, the study reveals that more research regarding how digital transformation interventions impact the effectiveness of these technologies for industrial supply chains must be understood. Methods: A structured survey was applied to 181 supply chain managers in manufacturing firms scattered across Jordan. Results: The findings using SmartPLS for statistical analysis indicated that automation has the strongest positive effect on supply chain performance, followed by data integration. But digital technology did not have a significant direct effect, unless it was accompanied by broader digital transformation initiatives. Conclusions: Theoretically, this study reinforces digital transformation theory as a vital framework, whereas in practice, it invokes the strategic deployment of automation and integrated data application designs to underpin supply chain efficiency and competitiveness. Finally, this study offers practical guidance for practitioners who seek to employ the use of digital transformation in the current dynamic business environment.

The Fast Moving Consumer Goods (FMCG) sector is a critical component of the global economy, providing consumers with a wide range of products that are consumed daily. However, this sector faces vulnerability during disruptions. Therefore, this research amid to examine the effect of supplier trust and integrated technology on supply chain resilience (SCR) for sustainable supply chains in the FMCG sector. Data was collected from 409 respondents from the FMCG sector in the United States. The hypotheses were tested using Structural Equation Modeling through SmartPLS. The study findings found that all the direct and mediating hypotheses were supported. The findings suggest that better supplier trust and the use of integrated technology enhance the capability of an organization to better respond to disruptions. It makes the supply chain more resilient. Further, SCR brings sustainable supply chains to the FMCG sector. Similarly, SCR is a significant mediator in the relationships between independent and dependent variables, highlighting the importance of resilient supply chains. This research contributes to the literature on study variables and the Dynamic Capability View theory, as supplier trust integrated technologies are crucial factors in building resilient supply chains. This research has several managerial implications, including managers’ need to prioritize building trust with suppliers to facilitate increased information sharing. They should invest in integrated technologies to sense, forecast and be proactive in building SCR and, eventually, a sustainable supply chain.

The increasing urgency of addressing climate change has necessitated the adoption of innovative technologies to monitor and mitigate carbon emissions and waste throughout the supply chain. This paper explores the role of digital tools and artificial intelligence (AI) in enabling real-time tracking and analysis of environmental impact at each stage of the supply chain. By integrating IoT devices, big data analytics, and machine learning algorithms, organizations can gain comprehensive insights into their operations, identifying key areas where emissions and waste can be reduced. The use of AI-powered predictive analytics allows companies to model various scenarios, optimizing resource allocation and operational efficiency while minimizing environmental footprints. This research also highlights successful case studies where companies have implemented these technologies, resulting in significant sustainability improvements and cost savings. Furthermore, the paper discusses the challenges associated with data integration, system interoperability, and the need for industry-wide standards to ensure effective monitoring and reporting. The importance of stakeholder collaboration is emphasized, as engaging suppliers, customers, and regulatory bodies is essential for achieving comprehensive sustainability goals. Ultimately, this paper advocates for the strategic implementation of digital tools and AI as pivotal enablers of sustainable supply chain management, providing organizations with the capability to adapt to changing environmental regulations and consumer expectations. By leveraging technology to monitor and reduce carbon emissions and waste, businesses can enhance their competitiveness while contributing to a more sustainable future.

The review delves into the pivotal role of technology in revolutionizing supply chain risk management practices within the logistics sector. The paper explores the myriad of risks faced by modern supply chains, ranging from natural disasters and geopolitical tensions to cyber threats and disruptions in global trade patterns. It investigates how technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and predictive analytics are reshaping traditional risk management approaches by providing real-time visibility, data-driven insights, and proactive mitigation strategies. Through a comprehensive analysis, the study examines the transformative potential of these technologies in enhancing supply chain resilience, agility, and responsiveness to unforeseen disruptions. It highlights the benefits of leveraging blockchain technology for secure and transparent supply chain transactions, IoT sensors for real-time monitoring of goods in transit, AI algorithms for predictive risk modeling, and predictive analytics for identifying and mitigating potential disruptions before they escalate. Furthermore, the paper delves into the challenges and complexities associated with adopting and integrating these technologies into existing supply chain processes, including interoperability issues, data privacy concerns, and the need for specialized expertise. By providing practical insights, case studies, and best practices, the study aims to empower logistics professionals, policymakers, and industry stakeholders to harness the full potential of technology-driven solutions in managing supply chain risks effectively and ensuring business continuity in an increasingly uncertain and volatile global environment. Keywords:  Technology, Supply Chain, Risk Management, Innovations, Challenges, Logistics.

No abstract available

As global supply chains become more intricate, the significance of supply chain risk management has significantly increased. This research delves into the utilization of artificial intelligence (AI) in managing supply chain risks, analyzing cutting-edge advancements, obstacles, and potential areas for further exploration. Through a comprehensive review of literature sources like Google Scholar, Web of Science, EI, and Scopus, this study investigates how AI methods such as machine learning, deep learning, neural networks, fuzzy logic, genetic algorithms, and evolutionary algorithms can help mitigate supply chain risks. These AI technologies have demonstrated remarkable efficacy in mitigating various risks, including forecasting, anomaly detection, image recognition, text mining, logistics optimization, and emergency response tactics. Apart from AI-driven approaches, optimization solvers and algorithms play a critical role in tackling complex supply chain dilemmas. Mathematical programming solvers, including linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP), are commonly used to model and optimize supply chain networks by considering factors like cost, capacity, and demand fluctuations. Fine-tuning solver parameters and strategies to enhance computational efficiency—known as solver tuning—has been crucial in enhancing solution quality and decreasing computation time for large-scale supply chain issues. Heuristic solvers like genetic algorithms, simulated annealing, and ant colony optimization are frequently employed to resolve conflicts in supply chains. These solvers offer practical solutions to problems where exact methods are computationally impractical, allowing for swift responses to disruptions such as production delays or spikes in demand. Pyramid classification, a hierarchical approach, further refines decision-making by categorizing risks and aligning response strategies based on priority and severity. Furthermore, the integration of AI technologies and solvers enables advanced conflict resolution techniques, including scenario-based modeling and multi-objective optimization. These approaches enable decision-makers to weigh trade-offs between conflicting objectives like minimizing costs versus maximizing service levels in real-time. The study underscores that AI technologies and optimization solvers significantly bolster risk management in supply chains. Nonetheless, challenges such as data privacy concerns, security vulnerabilities, technical intricacies, and implementation obstacles pose critical barriers to widespread adoption. The study offers practical suggestions for businesses and decision-makers while pinpointing key areas for future exploration, such as devising hybrid models that merge heuristic solvers with AI for adaptive and scalable risk management strategies.

No abstract available

The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two isolated areas, i.e. the impact of digitalisation on SC management (SCM) and the impact of SCM on the ripple effect control. To the best of our knowledge, this is the first study that connects business, information, engineering and analytics perspectives on digitalisation and SC risks. This paper does not pretend to be encyclopedic, but rather analyses recent literature and case-studies seeking to bring the discussion further with the help of a conceptual framework for researching the relationships between digitalisation and SC disruptions risks. In addition, it emerges with an SC risk analytics framework. It analyses perspectives and future transformations that can be expected in transition towards cyber-physical SCs. With these two frameworks, this study contributes to the literature by answering the questions of (1) what relations exist between big data analytics, Industry 4.0, additive manufacturing, advanced trace & tracking systems and SC disruption risks; (2) how digitalisation can contribute to enhancing ripple effect control; and (3) what digital technology-based extensions can trigger the developments towards SC risk analytics.

Modern food supply chains are intrinsically sophisticated due to their multi-participant and multi-echelon structure, which are challenging to handle high turbulent business environment. The development of Perishable Food Supply Chains (PFSC) has to be strong enough to manage any type of disruptions in the food industry. At the same time, the food processing industry must also take responsibility for the social and environmental consequences of their deeds. This has further led to performance deterioration and intensified design complexity. Recently, digitalization and Blockchain technology (BCT) have brought unfathomed rebellions in PFSC. Despite the potential and market hype, the application of BCT to track the perishable products and status of in-transit shipments is still a challengingtask for the food industry due to privacy and security issues, restricted transactional and scalability performance, deficiency of industry standards and managerial abilities, etc. However, integrating the BCT with the eventual benefits of the Internet of Things (IoT) (i.e., Chain of Things (CoT)) increases the performance of good traceability in any supply chain. The proposed CoT-based Track and Trace system (CoT-TTS) employs a set of IoT devices, BCT, and Adaptive Neuro-Fuzzy Inference System (ANFIS). The performance of CoT-TTS is evaluated through a case study using an EOSIO platform. The effectiveness of the proposed system is evaluated in terms of depth, breadth, access, and precision of the transactions.

The integration of Artificial Intelligence (AI) into supply chain management has emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary business operations. This paper explores various theoretical approaches to AI within the context of supply chain optimization, delineating pathways to achieve heightened performance and adaptability. Commencing with a historical overview, the paper delves into the evolution of AI techniques in supply chain management, elucidating how these methodologies have transformed the landscape of logistics and operations. Fundamental to this exploration is the discussion on mathematical modeling and algorithmic frameworks that underpin supply chain optimization, providing the theoretical foundation for subsequent AI applications. A key focus of the paper lies in the application of machine learning techniques for demand forecasting and inventory management, which leverage data-driven insights to optimize resource allocation and mitigate risks associated with supply-demand fluctuations. Additionally, network theory and graph algorithms play a crucial role in optimizing the structure and dynamics of supply chain networks, enabling efficient transportation, distribution, and inventory routing. Strategic decision-making in supply chains is addressed through the lens of game theory, which offers theoretical frameworks to model interactions among multiple stakeholders and optimize outcomes in competitive environments. Moreover, swarm intelligence and multi-agent systems provide innovative solutions for coordination and collaboration within complex supply chain ecosystems. Evolutionary algorithms and artificial neural networks are discussed as powerful tools for supply chain design, predictive analytics, and risk management, offering capabilities for optimizing decision-making processes across various operational domains. Furthermore, reinforcement learning techniques empower dynamic decision-making in real-time operational settings, fostering adaptive and resilient supply chain management practices. By integrating multiple AI techniques, hybrid approaches offer synergistic solutions that capitalize on the strengths of diverse methodologies to address multifaceted challenges in supply chain optimization. Through a synthesis of theoretical insights and practical case studies, this paper provides valuable insights into the current state and future directions of AI-driven supply chain optimization.

No abstract available

Whilst there has been previous work focused on the role of technologies in enhancing supply chain risk management and, through such an enhancement, increased competitive advantage, there is a research gap in terms of understanding the links between external institution pressures and internal adoption factors. We use institutional theory (IT) and the resource based view (RBV) of the firm to address this gap, developing a framework showing how a proactive technology-driven approach to supply chain risk management, combining both external with internal factors, can result in competitive advantage. We validate the framework through analysis of quantitative data collected via a survey of 218 firms in the manufacturing and logistics industry sectors in India. We specifically focus on the technologies of track-and-trace (T&T) and big data analytics (BDA). Our findings show that firms investing in T&T/BDA technologies can gain operational benefits in terms of uninterrupted information processing, reduced time disruptions and uninterrupted supply, which in turn gives them competitive advantage. We add further novelty to our study by demonstrating the moderating influences of organisational culture and flexibility on the relationship between the technological capabilities and the operational benefits.

The COVID-19 (Corona virus disease 2019) pandemic continues to slash through the entire humanity on the earth causing an international health crisis and financial uncertainty. The pandemic has formed a colossal disruption in supply chain networks. It has caused piling higher mortality in patients with comorbidities and generated a surging demand for critical care equipment, vaccines, pharmaceuticals, and cutting-edge technologies. Personal protective equipment, masks, ventilators, testing kits, and even commodities required for daily care have been scarce as lockdown and social distancing guidelines have kicked in. Amidst COVID-19, implementing and executing key processes of the healthcare supply chain (HSC) in a secured, trusted, effective, universally manageable, and the traceable way is perplexing owing to the fragile nature of the HSC, which is susceptible to redundant efforts and systemic risks that can lead to adverse impacts on consumer health and safety. Though the crisis shone a harsh light on the cracks and weaknesses of the HSC, it brings some significant insights into how HSC can be made more resilient and how healthcare industries figure out solutions to mitigate disruptions. While there are innumerable experiences learned from the disruption of this crisis, in this paper, five important areas to analyze the most vital and immediate HSC enhancements including building a resilient supply chain, thinking localization, implementing reliable reverse logistics, breaking down extant silos to achieve end-to-end visibility, and redesigning HSC using digitalization are emphasized. This work identifies important features related to CoT and HSC. Also, this study links these lessons to a potential solution through Chain of Things (CoT) technology. CoT technology provides a better way to monitor HSC products by integrating the Internet of Things (IoT) with blockchain networks. However, such an integrated solution should not only focus on the required features and aspects but also on the correlation among different features. The major objective of this study is to reveal the influence path of CoT on smart HSC development. Hence, this study exploits (i) fuzzy set theory to eliminate redundant and unrelated features; (ii) the Decision-Making and Experimental Evaluation Laboratory (DEMATEL) method to handle the intricate correlation among different features. This fuzzy-DEMATEL (F-DEMATEL) model attempts to direct CoT technology towards smart HSC by identifying the most influencing factors and investors are recommended to contribute to the development of application systems. This work also demonstrates how CoT can act a vital role in handling the HSC issues triggered by the pandemic now and in the post-COVID-19 world. Also, this work proposes different CoT design patterns for increasing opportunities in the HSC network and applied them as imperative solutions for major challenges related to traditional HSC networks.

Differentiated production and supply chain management (SCM) areas benefit from the IoT, Big Data, and the data-management capabilities of the AI paradigm. Many businesses have wondered how the arrival of AI will affect planning, organization, optimization, and logistics in the context of SCM. Information symmetry is very important here, as maintaining consistency between output and the supply chain is aided by processing and drawing insights from big data. We consider continuous (production) and discontinuous (supply chain) data to satisfy delivery needs to solve the shortage problem. Despite a surplus of output, this article addresses the voluptuous deficiency problem in supply chain administration. This research serves as an overview of AI for SCM practitioners. The report then moves into an in-depth analysis of the most recent studies on and applications of AI in the supply chain industry. This work introduces a novel approach, Incessant Data Processing (IDP), for handling harmonized data on both ends, which should reduce the risk of incorrect results. This processing technique detects shifts in the data stream and uses them to predict future suppressions of demand. Federated learning gathers and analyzes information at several points in the supply chain and is used to spot the shifts. The learning model is educated to forecast further supply chain actions in response to spikes and dips in demand. The entire procedure is simulated using IoT calculations and collected data. An improved prediction accuracy of 9.93%, a reduced analysis time of 9.19%, a reduced data error of 9.77%, and increased alterations of 10.62% are the results of the suggested method.

No abstract available

Background: Supply chain performance measurement is an integral part of supply chain management today, as it makes many critical contributions to supply chains, especially for companies and supply chains to identify potential problems and improvement fields, evaluate the efficiency of processes, and enhance the health and success of supply chains. The purpose of this study is to contribute to future research and practical applications by presenting a more standard, comprehensive, and up-to-date measurement scale developed based on the SCOR model version 13.0 performance measures in the disruptive technology era. Methods: The study was performed in seven stages and the sample size consists of 227 companies for pilot data and 452 companies for the main data. The stages comprise item generation and purification, exploratory factor analysis for the pilot study and main study, confirmatory factor analysis for the main study, convergent, discriminant, and nomological validity appraisal, and investigation of bias effect. Results: The scale was developed and validated as a five-factor and thirty-one item structure. Conclusions: Some key trends and indicators must be followed today to perceive the landscape of future supply chains. This measurement scale closely follows the future supply chains. Additionally, the findings have been confirmed by the contributions of disruptive technologies and the conceptual structure of supply chain management.

The present study looks at how the internet of things and blockchain technology might be used to improve prospects in supply chain procedures. The current pandemic has highlighted the significance of resilient and dependable supply chain systems that are less dependent on humans and more efficient in cycling goods supply chains. The present study included and excluded records from two recognised databases, Scopus and Web of Science, using the PRISMA declaration 2020. After following the inclusion and exclusion criteria details, investigated the forty-seven articles with two significant data streams (traceability of supply chain management, resin and sustainability). Results illustrated that in today's environment, the rivalry has shifted from "firm vs firm" to "supply chain vs supply chain." As a result, the ability to optimise the supply chain has arisen as a significant issue for organisations seeking a competitive advantage. However, it has become increasingly challenging to traceability of products and merchandise while they are moving through the value chain network. The Internet of Things (IoT) applications and blockchain technologies can help companies observe, track, and monitor products, activities, privacy, security and processes within their respective value chain networks. Other applications of IoT include product monitoring to optimise operations in warehousing, manufacturing, food supply chain and transportation. Combined with IoT, Blockchain technology can enable various application scenarios to enhance supply-chain transparency and trust. When combined, IoT and Blockchain technology can increase the effectiveness and efficiency of modern supply chains. First, we illustrate how deploying Blockchain technology in combination with IoT infrastructure can streamline and benefit modern supply chains and enhance value chain networks. Second, we also identified that the resilience of big data analytics, machine learning and artificial intelligence is helpful for the sustainable development of social, economic and environmental contexts.  

In the rapidly evolving landscape, digitising the operations and facilities in a supply chain network is essential to make the system autonomous and develop strategies for enhancing resilience, transparency, and efficiency. The COVID-19 pandemic highlights the necessity of sustainable solutions for the hybrid mode of operations. To overcome several challenges, including price optimisation, demand forecasting under uncertainty, supply-demand gap reduction, take into account vulnerability, competitive business environment and risk, the supply chain needs to be streamlined with technology-driven infrastructures incorporating physical and information flow into overall supply chain processes. The digitisation aspect encompasses adopting cutting-edge technologies such as enterprise resource planning (ERP) for supply chain visibility, e-hailing platforms, real-time data analytics, the Internet of Things (IoT) and Internet of Behaviour (IoB), blockchain-driven technology, as well as additive manufacturing, enabling seamless connectivity and communication among diverse stakeholders. This revolution enables strategic integration of various entities and state of the art data-driven decision-making, providing real-time insights into logistics movements, demand forecasting, production planning and inventory levels. Supply chain digitisation and management emphasises collaboration with supply chain partners to identify important factors, optimise costs and enhance overall supply chain resilience. Digitisation and management are technological evolutions and strategic shifts integrating analytical tools, allowing businesses to formulate models to improve performance. The implementation of blockchain-driven technology solidifies trust and safety transactions by creating an immutable and transparent log, mitigating threats and enhancing traceability. Digitisation and management exemplify a transformative journey towards a more connected, data-driven, and agile global supply chain ecosystem.

In practice, an increased interest into end-to-end visibility as a future-oriented driver and capability of resilient supply chains can be observed. However, the research in this area is in its infancy. Even less is understood about resilience and the potentials of a digital supply chain in pandemic settings. Based on an analysis of the relevant literature supplemented with the multiple case studies constructed with the use of primary data, we build a framework that could be instructive for supply chain managers seeking to manage resilience during pandemic disruptions and using digital technology. Our main methodological contributions are unlocking the value and potentials of end-to-end supply chain visibility for resilience management in the face of pandemic disruptions. We propose an associated design and implementation framework containing multiple dimensions—management, organizational, and technological. The outcomes of the article offer a conceptual guideline concerning the potentials and implementation of end-to-end visibility in the management of supply chain resilience.

No abstract available

Numerous global supply chains lack the tools necessary to survive in the world. Because supply chain managers must change their focus from cost-cutting to enabling new processes, as well as making businesses more connected and flexible, in order to create value across the enterprise. Digitalization controls the networks that transforms today’s supply chains. However, supply chain management consists of intricate systems and procedures that span numerous ecosystems, partners, and geographical regions and are managed by a number of different stakeholders. The complexity of current supply chain management systems has resulted in a lack of effectiveness along the whole value chain, which delays transactions and erodes customer and organization trust globally. Therefore, in order to consider this concept to be examine, empirical research was conducted to assess the blockchain technology integration and internet of things and their impact on digital supply chain transformation. This research used a quantitative approach, descriptive and analytical methods used for assessment as well as a convenient random sampling technique. Data of 187 respondents used after screening 390 received responses from hardware manufacturing companies located in Dubai UAE. Data was tested using SmartPLS 4.0 by applying, reliability, validity, discriminant validity and hypothesis testing. The findings revealed a positive significant relationship between blockchain technology and digital supply chain transformation utilizing internet of things in an upgrading way.

Abstract The aim of this document is to present a full description of the innovative efforts that have been made over time to develop digital technologies to manage the interfaces between supply chain management and marketing processes, and beyond that the role they play in maintaining supply chain management and marketing (SCM-M). The methodology used is patent analysis, which collects data from six different offices (USA, China, Taiwan, Japan, South Korea and Germany) and uses real samples to perform this analysis. The research also learned of a number of key security technologies related to the Fourth Industrial Revolution (Industry 4.0) that are considered more relevant for the effective integration of SCM-M (i.e.me. Industrial Internet of Things and Cloud Computing). The results of this study provide detailed information on the digital technologies that support SCM-M integration. Concretely, the authors compose the role dispute related to information, storage and elaboration for the integration of SCM-M by wishing on illustrative realities. In addition, the authors present the organizations most involved in the development of digital technologies for SCM-M integration over time and provide evidence of the impact of these technologies in terms of their impact on resulting technology developments. JEL classification numbers: J43. Keywords: Innovation, Marketing, Supply Chain Management, Internet of Things, Cloud Computing, Patent Analystics, Industry 4.0, Integration marketing-supply chain management.

The objective of the study is to develop a comprehensive scale and empirical validation of the model as an instrument for validity assessment of Digital Supply Chain Management (DSCM) dimensions by considering decision factors in determining supply performance. Based on the study of Indian production facilities, this paper studies five supply chain performance attributes: reliability and responsiveness for operational vs strategic perspectives; agility for flexible operations; cost as a management approach to identifying key processes potentially impacting costs at multiple levels within a system and assets utilisation for managing resources. The data were collected by a structured questionnaire from 152 employees to support the literature-based model investigating DSCM processes and their performance areas. The data was processed through Structural Equation Modeling (SEM) and Factor Analysis as an important implication for supply chain managers and researchers interested in optimising performance measures in the Indian manufacturing domain.

Background: Emergency scenarios present unprecedented challenges for supply chains worldwide, particularly in the management and distribution of critical supplies, where timely delivery and maintaining integrity are crucial. Methods: This article explores an innovative approach to enhance the emergency management of supply chains using blockchain technology and simulation-based modelling. The proposed methodology aims to tackle issues such as transparency, efficiency, and security, which are vital for managing logistics during crises. A case study involving a vaccine rollout is used to demonstrate how blockchain can optimise supply chain operations, reduce bottlenecks, and ensure better traceability and accountability throughout the process. The case study is specifically developed based on the distribution of COVID-19 vaccines in Italy. Results: The integration of blockchain technology not only enhances data integrity and security but also facilitates real-time monitoring and decision-making. Conslusions: The findings suggest that the proposed blockchain-based model can significantly improve supply chain resilience in emergency situations compared to traditional methods, thereby offering valuable insights for policymakers and supply chain managers facing future crises.

The convergence of neuromorphic computing, digital twin technologies, and sustainable supply chain management presents unprecedented opportunities for transforming urban cyber-physical systems. This viewpoint paper introduces a novel conceptual framework that integrates neuromorphic computing architectures with digital twin methodologies to optimize sustainable IT supply chain operations within smart urban ecosystems. The proposed framework addresses critical issues related to real-time data processing, energy-efficient computation, and adaptive decision-making for green technology adoption across complex urban supply networks. Through theoretical analysis and conceptual modeling, we demonstrate how brain-inspired computing paradigms can enhance digital twin capabilities for monitoring, predicting, and optimizing supply chain sustainability metrics. The framework incorporates spatiotemporal knowledge graph embeddings, hybrid intelligence systems, and circular economy principles to create responsive, self-adapting supply chain networks. Our approach offers significant implications for urban planners, supply chain managers, and technology implementers seeking to advance computational sustainability science. The integration of neuromorphic processing units with digital twin architectures enables unprecedented energy efficiency improvements of up to 1000x compared to traditional computing approaches while maintaining real-time responsiveness for critical supply chain decisions. This research plays role in emerging urban sector computational sustainability by contributing a foundational framework for next-generation smart city supply chain management systems.

Digital twin technology has proven to be a transformative enabler for sustainable manufacturing by providing real-time virtual representations of physical assets and supply chain processes. This paper explores the integration of digital twins with agile supply chain strategies to improve the sustainability of manufacturing systems. By leveraging real-time data and advanced simulations, digital twins facilitate dynamic decision making, optimize resource utilization and reduce environmental impact. A case study is presented in which a digital twin is implemented with the aim of improving the responsiveness of agile supply chains and suggesting appropriate times for the delivery of components and the shipment of the final product, with the goal of minimizing the time components spend in warehouses. The analysis shows how digital twins improve clarity, adaptability and predictive capabilities, leading to greater efficiency and sustainability. The results show that the combination of digital twin technology and agile supply chain frameworks contributes significantly to resource optimization, emissions reduction and overall operational resilience. The proposed approach proves to be highly effective for various manufacturing environments, especially those that strive to balance efficiency and sustainability goals.

The rapid integration of digital technologies into logistics systems has reshaped the operational landscape of manufacturing supply chains, offering new opportunities for improving carbon efficiency and sustainability. This study investigates the impact of smart logistics technology adoption on supply chain carbon efficiency within the context of digital transformation in the manufacturing sector. Using a panel dataset of listed manufacturing firms from 2013 to 2023, we construct a comprehensive evaluation framework that integrates indicators of digital capability, logistics intelligence, and carbon performance. Employing a Difference-in-Differences (DID) and Double Machine Learning (DML) approach, the results reveal that smart logistics technology adoption significantly enhances supply chain carbon efficiency by optimizing energy utilization, improving logistics coordination, and reducing transportation emissions. Further mechanism analysis indicates that digital transformation amplifies these effects through data-driven decision-making, supply chain visibility, and real-time monitoring systems. Heterogeneity tests show stronger improvements among firms with higher digital maturity and in regions with supportive environmental regulations. In China, such environmental regulations include a range of regional policies—such as carbon trading schemes, green credit directives, and industrial emission standards—that directly or indirectly incentivize firms to adopt low-carbon logistics technologies. Provinces with more stringent regulatory frameworks, such as Jiangsu, Zhejiang, and Guangdong, have implemented mandatory carbon disclosure requirements and performance-based energy efficiency audits, which amplify the environmental and economic benefits of smart logistics adoption. This research contributes to the literature on digital transformation and sustainable supply chain management by providing empirical evidence of how smart logistics technologies can serve as a key pathway toward low-carbon and intelligent manufacturing systems. Policy implications suggest that encouraging digital infrastructure development and fostering green innovation ecosystems are essential for achieving carbon neutrality goals in industrial sectors.

In the context of Industry 5.0 and global decarbonization, this paper examines how Digital Twin (DT) technology restructures Supply Chain Management toward Green Logistics and Sustainable Supply Chain Management. Framed by the Resource-Based View, Dynamic Capabilities, and socio-technical systems theory, DTs are conceptualized not as static simulation tools but as strategic cyber-physical assets that reconcile efficiency with environmental performance. We analyze how high-fidelity virtual replicas, fed by IoT and 5G, enable real-time carbon monitoring, support circular reverse logistics, and facilitate multi-objective optimization of cost, service and emissions through advanced algorithmic models. The paper clarifies architectural requirements for “green” DTs and distinguishes their role from blockchain in ensuring data integrity. Drawing on cases from DHL, JD Logistics, IKEA and Microsoft, we show that DT integration lowers energy intensity, strengthens climate resilience, and generates auditable ESG data, and we outline an implementation roadmap addressing interoperability, data governance and the digital divide.

Digital Product Passports (DPP) offer an attractive route for research and development due to their integration across numerous industries. DPP presents a new angle on transparency and has the potential to improve compliance and sustainability in a variety of supply chains. This paper has conducted a comprehensive literature review about the historical aspects of DPP, its role in European sustainability goals and implementation requirements. The paper has developed a DPP enabled model for vertical and horizontal data integration of lithium-ion batteries across multi-level supply chains for electric vehicle supply chain. Moreover, the paper investigates the DPP realization for improving cooperation and smooth information exchange between producers, suppliers, retailers, and customers, hence supporting the fundamentals of a circular economy model. By shedding light on the supply chain’s vertical and horizontal facets, this model emphasises the critical role that data and technology integration play in achieving the objectives of DPP adoption. By include energy tracking during the consumption phase and covering the entire lifecycle from raw material procurement to end-of-life disposal, this model highlights the significance of a comprehensive and cooperative approach among stakeholders.

Purpose: This study examines the effect of information processing capabilities and digital supply chain integration on supply chain resilience considering the mediating effect of supply chain risk management in the context of the Indonesian manufacturing sector.Method: Study implements Partial Least Squares-Structural Equation Modeling (PLS-SEM) to analyze data from professionals in the manufacturing industry in Indonesia with respect to the relations between digital tools, risk management, and resilience.Findings: In latest study, the authors highlight how incorporating digital technology and managing for information are two key factors contributing to resilient supply chains, especially during periods of disruption. It highlights that companies using advanced technologies including real-time data analytics and cloud computing are in a better position to identify and manage risks, and therefore recover more quickly when disruptions occur.Novelty: These findings shed new light on the relationship between digital supply chain integration, information processing, resilience, and risk management in an emerging economy such as Indonesia. It builds on existing theories by exploring this dynamic within an industrial setting which has received less attention in the literature.Implications: The findings have important implications for practice in the field of manufacturing in Indonesia, indicating that the production companies need to invest in digital bases and a solid risk management system. These insights can help policymakers and industry leaders design robust and adaptive supply chains that can navigate effectively through global disruptions and uncertainties

Internal control is an important mechanism for all kinds of units to regulate activities, prevent internal and external risks, improve management efficiency and effectiveness, and guarantee the realization of unit goals. As the digital age has given new connotations to supply chain integration, the impact of supply chain integration on the quality of internal controls in manufacturing companies is becoming increasingly significant. The influence of digital-intelligent supply chain integration on the internal control quality of manufacturing businesses is examined theoretically in this paper using the literature analysis approach, and a corresponding improvement route is proposed. The study finds that supply chain integration using digital intelligence technology affects the quality of internal control of manufacturing enterprises in three aspects: information integration, operation integration, and organizational integration. The study's conclusion offers manufacturing companies theoretical direction on how to strengthen internal control through supply chain integration, which is crucial for their ability to transform, upgrade, and become more competitive in the age of digital intelligence.

Technological advancements have fundamentally reshaped the global supply chain landscape by improving transparency, traceability, efficiency, decision-making, and customer-centricity. With increasing globalization and rapidly changing customer expectations, supply chains must embrace digital transformation to remain competitive. This research paper explores how key technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Block chain, Big Data Analytics, Cloud Computing, and Robotics are integrated into various supply chain processes. It examines their impacts on forecasting accuracy, inventory optimization, logistics agility, demand responsiveness, and risk mitigation. The paper also highlights implementation challenges, organizational readiness, and strategic recommendations for successful adoption. Ultimately, the study concludes that technology-driven supply chains are not merely an option but a necessity for long-term competitiveness and sustainability.

This comprehensive article explores the intersection of data engineering and supply chain integration (SCI), examining how modern technological frameworks transform traditional supply chain operations. The article investigates the critical components of successful supply chain integration, including data consolidation, interoperability, real-time visibility, and predictive analytics. Through detailed analysis of implementation frameworks, applications, and challenges, the article demonstrates how data engineering serves as a foundational enabler for enhanced supply chain performance. The article examines various case studies across retail, manufacturing, and logistics sectors, highlighting practical applications and outcomes. Furthermore, it addresses emerging technologies such as blockchain, artificial intelligence, edge computing, and digital twins, providing insights into future directions of supply chain integration. The article contributes to both theoretical understanding and practical implementation of data engineering in supply chain management, offering valuable insights for organizations seeking to achieve operational excellence in increasingly complex business environments.

The development of artificial intelligence production and cloud computing triggered a transformative change, leading to modern supply chain patterns suitable for digital-era management. International trading systems with advanced supply chain technology must satisfy operational effectiveness requirements, speed, and reliability expectations. By using AI automation and cloud infrastructure, companies improve supply chain decision quality, gain better control of resources, and generate risk protection capabilities. Artificial intelligence automation enhances operations through its ability to deliver real-time observation predictions and wise automated decision processes. Contemporary machine learning methods accomplish simultaneous inventory prediction and detection of future delivery concerns through various processing operations, monitoring the market dynamics. These systems offer predictive analytics, leading to cost reductions and satisfied customers through fast deliveries and efficient supply chain clearing procedures. RPA operates as an AI automation service that executes complete sequences of activities, from order handling through warehouse management to delivery path setting, to enhance accuracy rates alongside performance evaluation capabilities. Cloud integration is an essential supply chain development mechanism because it enhances scalability and connectivity and offers increased flexibility. Instant data exchange operates through supply chain network convergence within cloud platforms, allowing corporate suppliers to connect with producers, distribution segments, and retail outlets. Cloud computing also creates a secure and protected business environment by linking protected data storage to strong access control capabilities. The pace of implementing Self-Managed supply chains accelerates when Cloud computer systems merge with Artificial Intelligence technology. Artificial intelligence-created digital twins enable public and private organizations to simulate business operations using artificial scenarios that generate real-time evolutionary results for strategic decision-making. Strategic business decisions benefit from strategic predictions and prescriptions from digital models linked to cloud-based computing systems. Organizations have achieved secure, maintained data systems and worldwide business transaction trackability through the fusion of artificial intelligence and blockchain technology. The present success exists because several unaddressed challenges stop organizations from implementing AI integration with cloud-based automation. High implementation costs, concerns over data privacy, and the need for skilled personnel pose significant barriers to adoption. Organizations must create detailed strategic approaches before building new infrastructure to address potential communication issues between traditional systems and contemporary cloud infrastructure. Organizations' competitive advantage emerges from their acceptance of AI-based automation and cloud integration solutions, which allowed them to boost operational supply chain efficiency and develop better agility and sustainability. The paper investigates how AI collaboration with cloud systems affects the automation of supply chains, specifically by assessing logistics advancements and operational advantages as well as anticipated industry obstacles for businesses working in evolving logistics environments.

Globalization and growing business dynamics lead to weakly harmonized supply chain (SC) systems. While smart technology offers innovation opportunities, supply chains often lack the integration needed to fully leverage resources and collaboration. A comprehensive systems engineering (SE)‐driven model for integrated innovation and optimization of smart SC business models is still missing. This study, through case research at SAP SE's Industry 4.0 division and three automotive companies, identifies key digital transformation objectives and interoperability gaps hindering smart opportunities. Systems engineering, supply chain management (SCM), and artificial intelligence (AI) methods were synthesized into a holistic SE‐driven model for transforming and optimizing SC business models. This model integrates management concepts like the theory of ambidexterity and dynamic capabilities, with SE methods capability engineering and complex adaptive systems, and semantic web concepts. Key SE contributions include meta‐modeling multi‐tier SC architectures, ensuring performance and resilience via simulations, and balancing value exploration and exploitation. Moreover, semantic harmonized and profit‐optimized SC ecosystems enable collaborative innovation for flexible, efficient manufacturing—a core Industry 4.0 principle. This SE‐driven model, validated by experts, provides a concise view of digital SC business models and a driver of generative design.

The combination of 5G and internet of things IoT technology is fundamentally a game-changer in smart logistics. This paper will consider the impact of 5G-enabled IoT networks on the entire supply chain, examining innovative network designs such as network slicing, multi-access edge computing (MEC), and low-power wide-area networks (LPWAN). In essence, the paper demonstrating that such technologies can increase warehouse efficiency by 20% and that the use of solutions rooted in narrowband IoT (NB-IoT) may help increase yard dispatch efficiency by 87%. Our systematic digital twin simulations and real-world tests have shown that bespoke network slices and edge computing are highly effective in meeting numerous logistics requirements. They improve throughput, strip off latency and enhance reliability. The paper provides specific, concrete information that IT professionals, telecom engineers, and supply-chain managers can apply in practice when implementing next-generation logistics solutions.

The increasing complexity of healthcare supply chain systems necessitates agile, data-driven, and cloud-based solutions to enhance operational efficiency, resilience, and patient-centered service delivery. This paper proposes an Agile-Driven Digital Transformation Framework designed to optimize cloud-based healthcare supply chain management systems through iterative development, adaptive planning, and continuous integration of digital technologies. The framework leverages cloud computing, artificial intelligence (AI), Internet of Things (IoT), and blockchain to achieve real-time visibility, predictive analytics, and traceability across procurement, inventory, and distribution networks. By embedding agile methodologies such as Scrum and DevOps within the transformation lifecycle, healthcare organizations can rapidly respond to disruptions, regulatory shifts, and fluctuating demand patterns—particularly during crises such as pandemics. The study explores the synergistic role of digital twins and data interoperability standards (e.g., HL7, FHIR) in fostering transparency and decision intelligence across multi-tier healthcare ecosystems. Additionally, it evaluates the challenges of digital adoption, including cybersecurity risks, data governance, and change management. The proposed framework provides a strategic roadmap for healthcare institutions aiming to modernize their supply chain infrastructure, enhance resource utilization, and ensure resilient, patient-safe delivery of medical goods and services in a cloud-empowered environment.

: Digital integration across supply chains offers significantly improved resource management and load balance in industrial enterprises. This study aims to examine the favorable effects of Industry 4.0 technologies, enterprise resource management systems, blockchain, and dynamic capabilities on resource efficiency and workload optimization. The research employs a descriptive literature analysis to identify principal themes, including sustainability, operational resilience, and performance enhancement, with an emphasis on green supply chain practices and agility. The paper portrays the limitations associated with fragmented research, the dynamic technology landscape, and supply chain procedures that are sometimes context-specific, thereby limiting their universal applicability. The findings support theoretical frameworks that illustrate the collaborative impact of digital technology in fostering sustainable and efficient supply chains. The research provides practical recommendations for manufacturing companies to efficiently leverage digital tools to maintain competitiveness, enhance decision-making, and foster resilience. We present a conceptual framework for the application of digital technology in supply chain processes and offer organized techniques to address resource optimization concerns. Research indicates that digital transformation of supply chains can effectively facilitate the achievement of sustainability objectives. Ultimately, it indicates prospective research avenues, including sector-specific analyses and emerging technologies like artificial intelligence and the Internet of Things, to enhance the adaptability and resilience of supply chains. This study offers a thorough foundation for resource management and optimization of load balancing through digital innovation in manufacturing supply chains.

Against the backdrop of escalating uncertainty in the global supply chain, steel enterprises are confronted with multiple risks such as dependence on iron ore imports, disruptions in continuous blast furnace production, blockages in bulk logistics, and demand fluctuations. Empirical data indicates that deep digital transformation can reduce the probability of disruptions by 40%-65%, shorten recovery time by 50%-75%, and reduce economic losses by 35%-60%. However, the effectiveness of transformation is moderated by three key factors: data governance level (breaking down silos in ERP/MES/SCM systems), organizational adaptability (establishing a flat emergency decision-making chain), and ecological collaboration breadth (co-building industrial cluster-level platforms). The study shows that digital transformation is the core path for steel enterprises to build resilient supply chains, but it requires simultaneous breakthroughs in technology integration, organizational change, and ecological cooperation bottlenecks to achieve a paradigm shift from passive risk resistance to active risk control.

Supply chain networks are global, multi-modal platforms that are expected to facilitate seamless exchange of physical goods, information across multiple industries and enterprises and stakeholders. Current supply chain information systems are limited in term of providing validated, real-time asset specific business relevant information during its lifecycle. Only one or few stakeholders have information access privilege causing both information asymmetry and inefficiencies in the $35T global supply chain market. Emerging technologies such as IoT and Blockchain democratize "trustworthy" data availability and empower stakeholders to make the right decision at the right time in the most cost-effective manner. Digital supply chain integration is progressively becoming a competitive differentiation of enterprises. Organizations leading to this approach of digital supply chain are rapidly improving their asset utilization and enabling new databased services. This paper presents the idea about relevance of complementary technologies like IoT and Blockchain technology for complete digitization of supply chain. The business case of pallet renting vendor is taken to showcase the use of technology integration to improve efficiency of its supply chain and asset management.

Digital Twin technology transforms managed transportation and supply chain optimization by creating virtual twins of physical assets and processes. This paper examines how Digital Twins supplement fleet management, real-time decision-making, and supply chain visibility by leveraging the best technology available today (Internet of Things, AI, and cloud computing This study presents simulation-based case analyses from both the pharmaceutical industry and logistics systems to conceptually demonstrate the potential benefits of Digital Twins, including cost savings, efficiency, and sustainability. However, there are still challenges and barriers to the wide-scale adoption of Digital Twins, such as issues related to data integration and methods, dangerous cybersecurity risks, and expensive upfront costs. The message from this research is that the market needs to embrace Digital Twins through incremental implementation, building up the data infrastructure, and letting machine learning and AI do the work. Future research into AI decision automation, Blockchain integration, and scaled replication is the next stage of research to consider. Overall, organizations must incorporate these recommendations to achieve the full potential of Digital Twins for smarter, more resilient, and sustainable logistics operations.

No abstract available

The smart infrastructure construction projects implemented under the Engineering-ProcurementConstruction (EPC) model have been growing more complicated with the globalized supply chains, the technology-induced complexity of the components employed, and the increased sustainability and risk management demands. Conventional EPC supply chain and project management have been highly divided, resulting in a lack of real-time visibility, reactive decision-making, and inefficiencies in performance. This paper opposes this by analyzing how digital supply chain practices can be incorporated into project management functions to enhance performance in EPC-based smart infrastructure projects. The research follows a conceptual and analytical course, integrating the latest literature on digital supply chains, project delivery of EPC, and technologies of smart infrastructure. It is on this synthesis that a Digital Supply Chain- Project Management Integration Framework is created with the aim of matching digital capabilities in terms of engineering, procurement, construction, and governance. Analyses of comparative performance based on literature-synthesized measures prove that digitally enabled EPC supply chains obtain significant improvements in comparison with the traditional practice in terms of approximately 35-40 % procurement lead time, 45-50 % cost variance, and 45-60 % risk response time. Other benefits can be seen in schedule reliability and supply chain visibility; the largest performance improvements can be seen within the construction phase and the project governance phase. The results indicate that digital integration contributes to the proactive management of risks, enhanced coordination, and performance-based project governance. The current study is an addition to the literature by filling the gaps between digital supply chain management and project management in the EPC setting and providing a practical understanding in furthering the resilience, transparency, and sustainability of smart infrastructure provision.

Digital Twin (DT) technology can be strategically integrated with Lean Supply Chain Management (LSCM) to improve operational excellence. A narrative literature review was undertaken using PRISMA methods to filter over 200 peer-reviewed articles (2020–2025) using Scopus, Web of Science, and ScienceDirect. The study uses the Technology Organization Environment (TOE) theory to describe a moderation paradigm in which technological readiness moderates the relationship between DT adoption and Supply Chain Performance. The results imply that DT-enabled lean systems offer better visibility, predictive analytics, and scenario modelling than reactive solutions. Integration makes lean proactive, improving efficiency, resilience, and sustainability. The paradigm fills a crucial research vacuum by explaining how DT improves lean's success while accounting for organisational and technology factors. Empirical testing is needed for this conceptual investigation, which will lay the groundwork for Structural Equation Modelling. Management should link DT expenditures with organisational preparedness to prevent digital waste and maximise strategic value, according to the report. This work advances academic scholarship and managerial practice by integrating DT, lean supply chain methods, and TOE contingencies into a theoretical model. It shows how digital technologies and lean concepts can digitise operational excellence and create more flexible, future-ready supply chains.

Nowadays, supply chains need to be more flexible and innovative due to the rapid development of technology, along with growing demands for high-quality products and more efficient services. The use of advanced automation technologies, such as the Internet of Things (IoT) and Digital Twins (DT), is the key to accomplishing these objectives. In particular, the food industry is gradually implementing these tools, and with it comes its prompt evolution. However, the extent and approach of this integration remain unclear. This research aims to elucidate the aforementioned issues through two main parts. First, a comprehensive review of the existing literature documented both the inadequacies of the global supply chain and the potential benefits of Digital Twins (DT) and the Internet of Things (IoT) in its operational processes. The second part of the study focused on assessing the level of automation in the Greek Food Industry using a questionnaire. The results were subjected to statistical analysis and compared with findings from similar studies on the European Food Industry. Ultimately, the study aimed to highlight the role of automation technologies in addressing the operational challenges of the supply chain and the optimization of the production processes in the Greek Food Industry.

Digital Twin technology has emerged as a transformative force in supply chain management, particularly in the optimization of transit routes through enhanced Control Tower capabilities. The integration of these sophisticated systems enables organizations to create virtual replicas of their physical supply chain networks, facilitating comprehensive monitoring, advanced analytics, and dynamic decision-making processes. Through variance-based route optimization, organizations can prioritize consistency and predictability over raw speed, leading to substantial improvements in delivery reliability and operational efficiency. The implementation of digital twins in supply chain control towers has demonstrated significant benefits across multiple dimensions, including inventory optimization, enhanced customer service, cost reduction, and improved supply chain resilience. By leveraging real-time data integration and advanced analytics, these systems enable proactive risk mitigation and dynamic routing adjustments, fundamentally transforming how organizations manage their supply chain operations. The continuous evolution of digital twin technology, particularly through enhanced AI integration and IoT connectivity, promises to further revolutionize supply chain management practices.

The objective of this review paper is to examine the integration of Artificial Intelligence (AI) and blockchain technology to enhance supply chain security and transparency, providing future-proof solutions for global industries. As the digital landscape evolves, traditional supply chain management methods face increasing vulnerabilities and inefficiencies. This paper reviews existing literature to assess the current challenges and potential benefits of integrating AI and blockchain in supply chains. The review highlights how AI enhances predictive analytics, demand forecasting, and operational efficiency, while blockchain ensures immutable, decentralized ledgers for real-time tracking and verification of goods. The synergy between these technologies offers unprecedented levels of transparency, enabling stakeholders to detect and respond to anomalies swiftly. Case studies across various industries demonstrate significant improvements in reducing fraud, counterfeiting, and human errors, alongside enhanced compliance with regulatory standards. Conclusions drawn from this review underscore the transformative potential of AI and blockchain in creating resilient, transparent, and secure supply chains. The integration of these technologies not only addresses current vulnerabilities but also lays the foundation for adaptive, intelligent supply chain networks capable of withstanding future disruptions. This paper provides actionable insights and practical frameworks for businesses and policymakers aiming to implement AI and blockchain solutions, emphasizing the importance of strategic planning, cross-industry collaboration, and continuous innovation in achieving long-term supply chain sustainability and security.

No abstract available

Digital Twins (DTs) are revolutionizing supply chain operations by creating dynamic digital replicas of physical assets, processes, and systems. This paper explores the integration of Artificial Intelligence (AI) with Digital Twins to bridge the physical and digital worlds in supply chain management. By leveraging AI, Digital Twins can analyze real-time data, predict future events, and optimize decision-making processes. This synergy enhances operational efficiency, reduces costs, and improves responsiveness to disruptions. We delve into the architecture of AI-driven Digital Twins, highlighting their components, data flow, and interaction mechanisms. Case studies across different industries demonstrate the practical applications and benefits of this technology. The discussion includes challenges such as data privacy, integration complexity, and the need for standardized protocols. Future research directions focus on advancing AI algorithms for better predictive capabilities and creating more robust, scalable Digital Twin frameworks. This paper underscores the transformative potential of AI-enhanced Digital Twins in creating agile, resilient, and intelligent supply chains. 

Background: This study examines how AI-driven metaverse integration enhances sustainable manufacturing performance in Jordan’s industrial sector, with particular emphasis on the mediating role of digital supply chain resilience. Grounded in resource orchestration theory (ROT), the research explains how digital twin systems, predictive AI analytics, and virtual collaboration technologies jointly support sustainability through improved supply chain agility, responsiveness, and continuity. Methods: Data were collected from 500 industrial managers, of which 415 valid responses were analyzed using partial least squares structural equation modeling (PLS-SEM). Results: The findings indicate that AI-powered metaverse dimensions have significant and positive effects on sustainable manufacturing performance, both directly and indirectly through digital supply chain resilience. The mediation analysis confirms that resilience serves as a critical mechanism linking metaverse-based technology adoption to sustainability outcomes. Conclusions: The study highlights the strategic importance of integrating advanced digital and virtual technologies into supply chains to address sustainability challenges, particularly in emerging economies such as Jordan. By extending resource orchestration theory to the metaverse context, this research contributes to theory development and offers practical insights for industrial managers seeking to leverage digital transformation as a source of sustainable competitive advantage.

As a crucial representation of new quality productive forces and a national strategic emerging industry, the high-quality development of the low-altitude economy is inseparable from the deep integration and empowerment of digital technology. Zhengzhou, as a national central city, has leveraged its “dual-zone overlay” strategic advantages to form a preliminary industrial ecosystem and application scale in the low-altitude economy. However, it also faces challenges such as uneven coverage of digital infrastructure, low marketization of data elements, shortage of technology and talent, and insufficient synergy in institutional mechanisms. Based on a review of relevant research and practices at home and abroad, this paper systematically analyzes the development foundation, digital advantages, and existing problems of Zhengzhou’s low-altitude economy. Subsequently, it proposes systematic countermeasures and suggestions, including building an “air-space-ground integrated” digital foundation, deepening the integration of “low-altitude + digital” scenarios, constructing a “technology-data-manufacturing” empowerment chain, and improving the digital ecosystem and institutional safeguards. This aims to provide theoretical references and practical pathways for Zhengzhou to seize digital opportunities and promote the higher quality, more efficient, and more sustainable development of its low-altitude economy.

In that regard, the digital transformation has proved to be a strategic requirement in the resilience and competitiveness in the procurement and supply chain management. The subsequent study is targeted at exploring the possibility of AI, IoT, and data analytics as a system to enable organizations create agile, transparent, and efficient supply networks that can be resilient in nature. The paper discloses the results of the qualitative and exploratory research, which is grounded on the publications indexed in 2015-2025 in Scopus on how such technologies can ensure operational resilience based on predictive analytics, real-time visibility, and informed decisions. Applications of technological integration in the real world are: Unilever artificial intelligence (AI) demand forecasting, smart containers with IoT at Maersk, Digital Factory at Siemens and Watson Supply Chain at IBM. The most striking information was that the implementation of AI has been accelerating remarkably over the recent past as an embodiment of the shift to the proactive supply chain strategy. There are still the barriers to interoperability, cybersecurity, and the absence of digital competence.

The digital era really changed the face of supply chain quality management. This article explores the transformative power of some emergent technologies, including the Internet of Things, Artificial Intelligence, Machine Learning, and Blockchain, on a global supply chain’s practice. In this context, while the challenges of managing and maintaining globalization are at an all-time high for companies, this set of technologies offers unprecedented opportunities for visibility, accuracy, and efficiency imperatives in a supply chain. Digital innovations have become vital to mitigate risks, optimize processes, and maintain high standards of quality by facilitating real-time data collection, predictive analytics, and decentralized ways of information sharing. The article examines the integration of these technologies within traditional supply chain management frameworks, identifying their potential to drive sustainable competitive advantage. However, it also looks at the challenges created through digital transformation, including data security concerns, system interoperability, and growing pressure to innovate more.

Global supply chains are increasingly complex, with issues of transparency, trust and efficiency leading to fraud, counterfeiting and delays. The industry 4.0 relies on the IoT to be tracked on the fly and blockchain offers immutability and decentralized trust. Their production offers a new way to safe and open supply chain management. The present paper discusses blockchain-IoT applications in pharmaceuticals, food traceability and logistics, and enhances traceability, efficiency, and fraud prevention. Other challenges, including scalability, energy use, and interoperability, however, persist. Interoperability standards and lightweight consensus, the addition of edge computing and the integration of AI should be further explored. Overall, the IoT blockchain integration can help create trust and real-time visibility that will ensure the safety, resilience, and sustainability of the supply chain in the digital world.

The integration of Artificial Intelligence (AI), the Internet of Things (IoT), Blockchain, 5G, and Cloud Computing is revolutionizing supply chain management, enhancing efficiency, transparency, and resilience. This study explores Vietnam’s strategic approach to adopting these technologies, emphasizing the necessity of advanced infrastructure, workforce development, and adaptive regulatory frameworks. Key policy recommendations include investing in resilient data centers, expanding 5G networks, and fostering public-private partnerships to create a robust digital ecosystem. Additionally, the study highlights the importance of education and vocational training in AI, cybersecurity, and digital supply chain management to bridge the skills gap. Regulatory improvements in data protection, electronic transactions, and intellectual property rights are essential for mitigating risks and encouraging technological innovation.

Abstract This research develops and tests a unified framework combining Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain to tackle major inefficiencies in pharmaceutical reverse logistics, a sector plagued by high costs and fraud. The study designed and implemented a system using a Random Forest model for fraud detection, an LSTM network for forecasting return volumes, and a Genetic Algorithm for optimizing collection routes. Experimental results demonstrated significant improvements: fraud detection accuracy reached 87%, return forecasting error was reduced to 12%, and route optimization cut costs by 18%. The findings prove that integrating these technologies can simultaneously boost operational efficiency, ensure regulatory compliance, and reduce waste. This work provides a validated blueprint for modernizing supply chains and highlights key future areas like Edge AI and post-quantum blockchain for further advancement. Keywords: AI, IoT, Blockchain, Supply Chain, Pharmaceutical Logistics, Optimization.

This paper presents an analysis of the ways in which Blockchain and Internet of Things (IoT) systems can be combined for the betterment of supply chain management as well as a discussion of the increased security, transparency and/or continuity of business that may result from such an integration. In the current world where the global supply chains are continuing to expand and integrate, it has been noted that there is need to protect real time information flows and enhance the integrity and traceability of information. Using Blockchain’s decentralized and immutable structure together with IoT’s data-collection aspect, this research explores ways in which supply chain processes could benefit from these technologies while minimizing the risks posed by data theft, forgery, and disruptions. The research employs published data analysis and a case study, encompassing cross-industry benchmarks, research, and empirical evaluation of Blockchain-IoT applications in industries. Survey data was obtained from recent implementations in industries including pharmaceutical, F&B and logistics. Quantitative tools and techniques were applied to evaluate the integrity and the reliability of Blockchain-IoT systems operations. Some insights discovered reveals that the integration between Blockchain and IoT fosters better real-time monitoring, minimizes counterfeiting and promotes better traceability of an average of 40% based on observed instances. Literature is advanced in this paper by presenting grounded data on Blockchain and IoT’s ability to maintain enduring and reliable supply chain security. The research implies that when the technologies are well deployed, they act as a key driver in facilitating secure and efficient supply chain. The presented research provides guidance on the adaptation of Blockchain and IoT to enhance the stability of the supply chain; it maps a tactical plan that industry participants can follow to achieve sustainable, secure supply chain operations.

This research empirically determines the synergetic roles of Artificial Intelligence (AI), Block Chain Integration (BCI) and Internet of Things (IoT) in enhancing supply chain transparency (SCT) and circular supply chain performance (CSCP), with an integration of reverse logistics efficiency (RLE) as a moderator, within the context of China's platform‐based economies. Focusing on two emerging circular sectors, urban mobility services and consumer electronics rental and refurbishment, this research employs a rigorous cross‐sectional quantitative research design to collect data from 800 industry professionals 400 from each sector using a structured questionnaire with validated multi‐item Likert scales adapted from established literature. Partial least squares structural equation modelling (PLS‐SEM) and multi‐group analysis (MGA) were applied via Smart‐PLS to examine direct, mediating, moderating, and moderated mediation effects. The outcomes reveal that BCI, AI and IoT significantly impact SCT, which positively affects CSCP, with RLE significantly moderating the impact of the analysis that reveals novel contextual variations: RLE and BCI are more impactful in urban mobility services, reflecting the reliance of the sector on securing data‐sharing for shared assets, while IoT puts a stronger direct effect on CSCP in consumer electronics, driven by real‐time product lifecycle tracking. The moderated mediation effects are more pronounced in the urban mobility sector, highlighting the contextual dependency of digital circular capabilities. The managers are being advised to adopt sector‐specific digital strategies, focusing on BCI in mobility and IoT in electronics and investing in reverse logistics as a strategic asset. The outcomes provide a groundbreaking perspective on leveraging digital technologies for sustainable supply chain transformation in platform‐based economics.

The rapid advancement of IoT technologies has emerged as a key driver of sustainable development, reshaping industries and societal structures. This study critically examines the intersection of IoT and sustainability by analyzing contemporary literature on the subject. A comprehensive review of IoT-driven innovations highlights their transformative impact across sectors such as agriculture, smart cities, and resource management. The research investigates how digitalization, particularly within supply chains, redefines operational strategies and enhances sustainability metrics. With the integration of technologies like RFID, blockchain, and IoT under Industry 4.0, organizations are revolutionizing process efficiency, transparency, and environmental responsibility. To assess these implications, the study conducts two comparative simulation experiments involving a three-party supply chain in cheese production—one utilizing traditional methods and the other leveraging IoT-based innovations. Results reveal significant improvements in order management efficiency and compliance handling, underscoring the critical role of emerging technologies in fostering sustainable practices. The proposed framework provides valuable insights into the broader management implications of IoT adoption, reinforcing its potential as a catalyst for global sustainability initiatives.

Abstract Industry 4.0 has revolutionised supply chain processes, providing companies with strategic advantages through advanced technologies such as cyber-physical systems, cloud computing, IoT, AI, and big data. This transformation aligns with the pursuit of high-level supply chain performance, achieved through cross-functional collaboration, end-to-end connectivity, real-time asset tracking, and increased decision transparency. However, the quantifiable extent of its influence has remained an open question, particularly in the context of integration and visibility. To address this gap, our research employs an empirical study, revealing that Industry 4.0 indirectly enhances supply chain performance by improving connectivity and integration within and between organisations, as well as enhancing information visibility and decision transparency. This research uncovers a novel sequential mediation by integration and visibility, offering valuable insights for scholars and industry practitioners alike.

This article addresses the dynamic landscape of smart supply chain management, characterized by the integration of cutting-edge technologies. It proposes an IoT-Blockchain system for monitoring equipment status in a smart supply chain environment. The system utilizes IoT sensors to collect temperature and humidity data from the equipment. This collected data is then processed and stored in the cloud using the InfluxDB database. To further enhance security and transparency in the monitoring process, the system incorporates blockchain technology. This ensures the tamper-proof nature of collected data through the deployment of smart contracts. The monitoring platform developed on Grafana provides users with an intuitive dashboard for accessing real-time information about the equipment’s status. The proposed system offers a comprehensive monitoring solution for sensitive supply chain operations, allowing stakeholders to track the equipment’s journey along the supply chain and monitor its status in real-time. This system has the potential to revolutionize supply chain monitoring, providing an efficient and secure way to optimize equipment performance and improve overall supply chain efficiency.

This study examines how Industry 4.0 (I4.0) technologies enhance supply chain resilience (SCR) in manufacturing firms by testing the mediating roles of supply chain agility (SCAG), supply chain adaptability (SCAD) and the moderating effect of customer integration (CI). Grounded in the Resource-Based View (RBV) and Dynamic Capabilities View (DCV), the research conceptualizes digital technologies—such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI)—as both strategic resources and enablers of dynamic capabilities in turbulent environments. Survey data were collected from 273 manufacturing firms in Turkey, a context shaped by geopolitical and economic disruptions, and analyzed using structural equation modeling (SEM). The results indicate that I4.0 technologies positively affect SCR directly and indirectly through SCAG and SCAD. However, while agility consistently strengthens resilience, adaptability shows a negative mediating effect, suggesting context-specific constraints. CI significantly amplifies the positive impact of I4.0 on SCR, underscoring the importance of external relational capabilities. Theoretically, this research advances supply chain literature by integrating RBV and DCV to explain how digital transformation drives resilience through distinct dynamic capabilities. Practically, it offers guidance for managers to combine digital infrastructure with collaborative customer relationships to mitigate disruptions and secure long-term performance. Overall, the study provides an integrated framework for building resilient supply chains in the digital era.

This paper explores the transformative impact of IoT and data analytics on supply chain operations, emphasizing their role in enhancing efficiency, reducing costs, and improving performance. It addresses key challenges such as lack of real-time visibility, inefficient inventory management, operational delays, and risk management. The integration framework involves data collection, processing, analysis, and decision-making. Emerging technologies like edge computing, blockchain, AI, 5G, and digital twins are highlighted for their potential to further revolutionize supply chains. Strategic recommendations include investing in IoT infrastructure, ensuring data security, fostering skill development, collaborating across stakeholders, and initiating pilot projects. The findings underscore the significance of IoT and data analytics in creating resilient, agile, and sustainable supply chains.

In the realm of supply chain management, the integration of sustainable practices alongside competitive efficiency is increasingly crucial. This study explores the convergence of cross-docking methodologies with advanced technologies such as IoT and AI to enhance the sustainability of supply chains. Cross-docking, known for its direct transfer approach, minimizes storage duration and operational costs, potentially reducing environmental footprints associated with traditional logistics. Concurrently, IoT enables real-time monitoring of goods and environmental parameters, while AI-driven analytics optimize logistics operations with precision. This integrated approach not only enhances operational efficiency but also underscores the pivotal role of technological innovation in fostering sustainable supply chain practices. Insights from this study contribute to advancing sustainable logistics strategies tailored to contemporary environmental imperatives and industrial competitiveness.

We have learnt that the supply chain management (SCM) has been improve by the Integration of IoT, blockchain, and other emerging technologies. This paper aims at discussing more recent development in managing the SCM with a reference to IoT real-time tracking, blockchain transparency, and optimisation frameworks. Such application of these technologies in vaccine distribution, agriculture supply chain and distribution of perishable products can illustrate how they can overcome modern SCM challenges. To support this evidence, simulation results and theoretical advancements of these innovations are provided.

Global food waste (1.3 billion tons per year) is a major economic and environmental issue, contributing considerably to cash losses and greenhouse gas emissions. This study assesses the efficacy, limitations, and integration potential of four Industry 4.0 technologies—IoT sensors, AI/ML algorithms, advanced active packaging, and blockchain traceability—for waste reduction at key food supply chain stages (production, logistics, retail, and consumption). We show that each technology has different waste reduction advantages using a rigorous literature synthesis (2020-2025), techno-economic evaluation, and environmental impact analysis. Crucially, coordinated deployment unleashes synergistic potential, resulting in considerably larger systemic waste reduction than standalone applications. However, fulfilling this promise requires overcoming long-standing obstacles such as implementation costs, data needs, recyclability issues, and energy usage. The results highlight the need for coordinated policy frameworks that promote interoperable technology, standardized data protocols, and circular design principles. This study outlines a systematic approach for changing food waste from a systemic failure to a controllable engineering issue, resulting in more resilient and efficient food systems.

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in logistics is transforming supply chain management (SCM) by enabling real-time data analysis, automation, and intelligent decision-making. These technologies are crucial for developing an intelligent supply chain that is adaptive, efficient, and future-ready. Traditional logistics systems face challenges such as inefficiencies in route planning, inaccurate demand forecasting, and poor inventory management, leading to increased costs and delivery delays. Additionally, fragmented data sources and a lack of predictive analytics hinder proactive decision-making in supply chain operations. To address these challenges, we propose an AI-based Smart Logistics Platform (AI-SLP), which leverages AI-driven predictive analytics and IoT-enabled real-time tracking to optimize route planning and inventory management. This platform integrates machine learning algorithms for demand forecasting, automated decision-making for dynamic scheduling, and IoT sensors for continuous monitoring of goods. The proposed AI-SLP enhances logistics operations by reducing transportation costs, minimizing delivery time, and improving inventory accuracy. Real-time insights enable proactive decision-making, ensuring better resource utilization and reduced wastage in supply chain processes. Findings from the proposed method demonstrate improved logistics efficiency, with a significant reduction in operational costs and enhanced customer satisfaction. The AI-SLP framework fosters a more resilient and adaptive supply chain, capable of responding to disruptions in real-time, making it a critical innovation for the future era of intelligent supply chain management.

The integration of the Internet of Things (IoT) in healthcare has revolutionized supply chain operations by enabling real-time monitoring, data-driven decision-making, and automation. This study proposes a secure and resilient IoT-enabled healthcare supply chain framework that enhances agility, integrity, and operational efficiency. The framework uses edge and cloud computing for real-time data aggregation, AI-driven predictive analytics for demand forecasting and anomaly detection, and blockchain technology for ensuring data security and traceability. Additionally, it employs renewable energy sources and low-power IoT protocols for sustainability in resource-constrained environments. The proposed framework uses renewable energy sources and low-power IoT protocols to guarantee viability in environments with limited resources. The results indicate substantial gains, including an 86.67% reduction in device authentication failures, a 91.67% decrease in data manipulation instances, an 88% decrease in illegal access attempts, and a 95% upgrade in data encryption security. Enhanced system resilience and availability metrics comprise a 7.31% increase in uptime and a 75% reduction in redundancy failures. The root mean square error and mean absolute error for the proposed framework are 0.28 and 0.03.

EDI currently remains one of the most significant technological formats that respond to contemporary rapid and connected business environment by ensuring real-time, efficient and accurate data exchange in supply chain management. This paper's central idea is the evolution of EDI from the early days of standard trade papers to contemporary technologies like blockchain, AI, ML, and IoT. Focusing on processes where these innovations add value for EDI, providing real-time data, improving security, and facilitating better decisions, this paper emphasises the essential functions of EDI in the modernisation of supply chains. In addition, it examines how EDI integration can help to minimise cost; manage stocks; and enhance business associations. Altogether, this paper showcases EDI’s positive effects on performance-enhancing aspects of the supply chain and outlines prospective applications of novel technologies to add further value to global supply chain processes.

Purpose: The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background: Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods: This research proposes an AI-enabled blockchain solution, ABISChain, designed to enhance the performance of supply chains. The system utilizes beliefs, desires, and intentions (BDI) agents to manage and prune blockchain data, thus optimizing the blockchain’s performance. A particle swarm optimization method is employed to determine the most efficient dataset for pruning across the network. Results: The AI-driven ABISChain platform demonstrates improved scalability, data consistency, and security, making it a viable solution for supply chain management. Conclusions: The findings provide valuable insights for supply chain managers and technology developers, offering a robust solution that combines AI and blockchain to overcome existing challenges in IoT-based supply chains.

The usefulness of technology integration in improving supply chain efficiency is investigated in this study. Technological developments in recent years have enabled automation, real-time tracking, data analysis, and enhanced communication, revolutionizing a number of industries, including supply chain management. This study looks at how important technologies like cloud computing, blockchain, artificial intelligence (AI), and the Internet of Things (IoT) might improve supply chain processes.

Technology plays a crucial role in revolutionizing supply chain management by enhancing integration, efficiency, and logistics operations. This study explores how emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), blockchain, and big data analytics optimize supply chain networks, reduce costs, and improve transparency. By analyzing real-world applications and addressing key challenges, this paper provides strategic recommendations for organizations to leverage technology for more resilient and efficient supply chains.

The research introduces Cloud-SCIM (Supply Chain Integration and Cloud-Based Operations Management to Resilient Smart Manufacturing) model, which aims to solve the problems of the modern manufacturing system. The model leverages cloud computing, IoT, and AI analytics to harmonize supply chain operations, boosting efficiency, flexibility, and resilience. Cloud-SCIM helps ensure effective production, demand projections, inventory maintenance, and sustainability by enabling real-time monitoring, predictive analytics, and automated decision-making. Performance indicators were compared and tested between the suggested model and traditional systems, including Overall Equipment Efficiency (OEE), Mean Absolute percentage Error (MAPE), and inventory turnover. The outcomes indicate significant changes: OEE has improved by 20 points (65 to 85), MAPE decreased by half (15 to 8), and inventory turnover has increased (5 to 9 times a year). Besides, Cloud-SCIM decreases the number of stockouts, downtime and consumes less energy as well as increases the effectiveness of risk mitigation. This study demonstrates that Cloud-SCIM can successfully modify smart manufacturing systems to provide a scalable, flexible solution that enhances operational productivity, resilience, and durability.

The traditional warehousing and logistics management model has been difficult to meet the needs of efficient operation, and the integration and optimization of intelligent warehousing and logistics information system has become the key. In this paper, an integrated optimization scheme of intelligent warehousing and logistics information system based on Internet of Things (IoT), big data, cloud computing and artificial intelligence (AI) is proposed. By designing a four-layer system integration architecture (data acquisition layer, data processing and analysis layer, application service layer and user interaction layer), the seamless connection and efficient cooperation between warehousing and logistics are realized. The optimization algorithm combines IoT, big data, cloud computing and AI technologies to optimize warehousing management and logistics operation with the goal of minimizing inventory cost, maximizing logistics efficiency and improving customer satisfaction. The empirical analysis takes E-commerce enterprise A as a case. Through one-year data collection and analysis, the results show that the optimized inventory is reduced, the times of excess inventory and shortage are reduced, the transportation path is more reasonable, the transportation time and cost are significantly reduced, the logistics distribution is more punctual and accurate, and the customer satisfaction is improved.

The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) is fundamentally transforming modern supply chain landscapes, offering solutions to increasing complexity brought by globalization, demand volatility, and dynamic market conditions. This article explores how the integration of these technologies enhances supply chain efficiency, transparency, and resilience through improved data collection, analysis, and decision-making capabilities. By examining the conceptual framework underpinning this technological synergy, various applications across warehousing, transportation, and risk management, and addressing implementation challenges including data quality, security, and scalability concerns, this article outlines the transformative potential of AI-IoT integration in creating more responsive, efficient, and resilient supply networks capable of navigating increasingly unpredictable global conditions.

This study aims to determine the impact of supply chain integration on supply chain performance and the Internet of Things (IoT) mediating role in Sindh Pakistan's textile industry. Primary data was gathered with the help of questionnaires from previous studies. The employees were requested to complete the questionnaire online, and the concerned HR department was officially contacted. To achieve the research objectives of this study, SmartPLS version 3 is applied. The findings of this study confirmed the direct effect of internal integration, supplier integration, and the Internet of Things on supply chain performance. In addition to this, the present study also confirmed the partial mediation effect of IoT between internal integration, supplier integration, and supply chain performance in the textile industry of Sindh, Pakistan, a developing country. This research uses RBV theory to examine the textile industry's supply chain and effectiveness. Internal integration, partner integration, IoT, and supply chain performance are discussed in the research. This clarifies how these technologies operate together to give organizations a competitive advantage. The study shows how the Internet of Things (IoT) is a go-between for integration and supply chain success. Textile business managers should consider investing money into IoT devices and using their benefits. Companies can get real-time information about their supply chain by using IoT devices and monitors.

This research study looks at how Fourth Party Logistics (4PL) and supply chain management (SCM) work together to improve operational efficiency and effectiveness. It looks into the integration of recent technological advances to improve 4PL capabilities within SCM systems. The paper investigates the role of new technologies such as IoT, AI, and blockchain in revolutionizing existing supply chain paradigms through a comprehensive literature review and case study analysis. Insights are presented into the obstacles, opportunities, and solutions for successfully integrating 4PLs through technology. Finally, this study contributes to a better understanding of how organizations can use technology to alter supply chain operations and gain a competitive advantage in today's changing business climate.

No abstract available

In response to the complex problems of path planning, infrastructure optimization and logistics flow management in digital supply chain networks, this paper introduces graph theory algorithms such as Dijkstra algorithm, Kruskal algorithm, and Edmonds-Karp algorithm, aiming to improve logistics efficiency, reduce operating costs, and enhance the supply chain's ability to cope with uncertainty. First, in the transportation network, each node represents a storage or production point, and the edge weight represents the transportation time or cost. This paper uses the Dijkstra algorithm to optimize the logistics path by calculating the shortest path between supply chain nodes. Then, the Kruskal algorithm is used to design the optimal infrastructure layout of the supply chain. The algorithm constructs a minimum spanning tree based on the weight of the edge, avoiding redundant paths while ensuring good connections between nodes, thereby improving the robustness and resource utilization efficiency of the supply chain. Finally, the Edmonds-Karp algorithm is applied to optimize the logistics flow of the supply chain. The algorithm solves the maximum flow problem to ensure smooth logistics flow between various links in the logistics network, reduce the occurrence of bottleneck problems, and improve the overall logistics capabilities of the supply chain. Through the Dijkstra algorithm, transportation time is reduced by 25% and logistics costs are reduced by 20%; through the Kruskal algorithm, infrastructure costs are reduced by 30%; after applying the Edmonds-Karp algorithm, the supply chain logistics flow has been increased by 35%, and logistics bottlenecks have been reduced by 40%. Through experimental verification, the graph algorithm proposed in this article can significantly improve the efficiency of the supply chain, reduce costs, and show good sustainability when considering green supply chain goals.

Demand forecasting is essential for streamlining supply chain operations in the digital economy and exceeding customer expectations. On the other hand, traditional forecasting techniques cannot frequently present real-time data and respond to dynamic changes in the supply chain network, leading to less-than-ideal decision-making and higher costs. This research aims to create a technique for optimizing the supply chain network based on blockchain-distributed technology (SCN-BT) to overcome these drawbacks and fully utilize the potential of the digital economy. The suggested framework uses the hybridized LSTM network and Grey Wolf Optimization (GWO) algorithm to examine demand forecasting in the supply chain network for inventory planning. The SCN-BT framework develops a safe and productive, enabling precise and flexible demand by combining blockchain with optimization techniques. A thorough case study utilized information collected from an enterprise supply chain that operates in the digital economy to show the efficiency of the suggested framework. Compared to conventional approaches, the results show considerable gains in demand forecasting precision, responsiveness of the supply chain, and cost-effectiveness. In the context of the digital economy, demand sensing and prediction enable firms to react to changes swiftly, shorten turnaround times, optimize inventory levels, and improve overall supply chain performance. The results highlight how blockchain technology has the potential to enhance collaboration, trust, and transparency inside intricate supply chain networks working in the digital economy. The experimental results show the proposed to achieve prediction rate of demand prediction rate of 128.93, demand forecasting accuracy ratio of 92.18%, optimum efficiency of 94.25%, RMSE rate of 1841.25, MAE rate of 260.74, and sMAPE rate of 0.1002 compared to other methods.

In order to maximize the benefit of building supply chain, the topology optimization method of building digital supply chain based on genetic neural network is studied. According to the overall structural characteristics of the building digital supply chain, customer demand data, supplier sales data, manufacturer production management data, and environmental policy exception data are collected to form a building digital supply chain data set. As input data, a building digital supply chain demand prediction model based on improved genetic LSTM neural network is constructed. Capture the needs of the building digital supply chain; according to the demand of suppliers, manufacturers and customers in building digital supply chain, a nonlinear 0–1 mixed integer programming model based on the newsboy model is established. According to the various information provided by the supplier, the ant colony algorithm is used to calculate the optimal supplier. After all suppliers are identified, the topology of the digital supply chain is constructed. So far, the optimization of the supply chain has been completed. The experimental data show that this method can accurately predict the demand of construction projects, the maximum error is less than 1.5%, and can obtain the best supplier selection results. Compared with before optimization, the profit of the structural digital supply chain after topology optimization increases the most.

In the context of globalization, complex supply chain networks have put forward higher requirements for the logistics and inventory management of enterprises. This paper proposes a supply chain optimization algorithm based on reinforcement learning, combining dynamic programming and deep neural networks to achieve real-time optimization of supply chain links. Based on the data of a multinational manufacturing company, mathematical modeling and simulation experiments are used to verify the advantages of the algorithm in reducing transportation costs and improving inventory turnover. This study demonstrates the potential of artificial intelligence technology in optimizing international supply chain networks and provides theoretical support and technical tools for enterprises to achieve efficient management.

No abstract available

In the evolving digital landscape, network flow models have become integral to various sectors, including supply chain management. This research develops a robust network flow model for semiconductor wafer supply chains, optimizing resource allocation and addressing maximum flow challenges in production and logistics. The model incorporates the stochastic nature of wafer batch transfers and employs a dual-layer optimization framework to reduce variability and exceedance probabilities in finished goods. Empirical comparisons reveal significant enhancements in cost efficiency, productivity, and resource utilization, with a 20% reduction in time and production costs and a 10% increase in transportation and storage capacities. The model’s efficacy is underscored by a 15% decrease in transportation time and a 6700 kg increase in total capacity, demonstrating its capability to resolve logistical bottlenecks in semiconductor manufacturing. This study concludes that network flow models are a potent tool for optimizing supply chain logistics and offer a 23% improvement in resource utilization along with a 13% boost in accuracy. The findings provide valuable insights for supply chain logistics optimization.

This paper introduces the "digital twin" to solve the problem of material allocation and real-time scheduling in the warehouse site. This project intends first to establish mathematical modeling based on a digital twin unmanned warehouse and dynamically optimize materials in the unmanned warehouse by combining visual analysis and deep reinforcement learning. Then, a security sharing mechanism of digital twin-edge network data based on blockchain fragmentation is proposed. For twin models with time-varying characteristics, a multi-node adaptive resource optimization method such as multipoint cluster selection, local base station consistent access selection, spectrum and computational consistency is constructed. This is done to maximize blockchain business processing power. A two-layer near-end strategy optimization (PPO) algorithm is proposed to solve the adaptive resource optimization problem. Experiments have proved that this method can significantly improve the overall processing power of the blockchain. In addition, this method is more adaptable than conventional deep reinforcement learning.

International procurement optimization and supply chain analytics play a crucial role in enhancing efficiency, resilience, and growth in West Africa. The region faces significant challenges, including infrastructural limitations, economic volatility, and regulatory complexities, which hinder seamless procurement and supply chain operations. This article explores how operations research, procurement analytics, and data-driven decision-making can address these challenges. It highlights strategies such as digital transformation, risk management frameworks, and regional collaboration to optimize procurement processes. By leveraging emerging technologies and best practices, businesses and policymakers can build a more resilient and efficient supply chain network, fostering economic growth and sustainability in West Africa.

In response to the problem of incomplete credit assessment caused by multi-source data silos in the current coal supply chain finance field, this study proposes an AI-enabled credit assessment and loan optimization system under multi-source data fusion. By constructing a four-dimensional data fusion layer (production-end sensor data, logistics GPS trajectory, transaction electronic invoice, and external price/policy data), using knowledge graphs to model the supply chain topology network, and using federated learning to achieve cross-enterprise data privacy protection. A hybrid AI evaluation model is designed, integrating LightGBM/XGBoost to process structured data and GNN to extract supply chain guarantee relationship characteristics, and combined with SHAP interpretable analysis to reveal that inventory turnover rate and price fluctuation sensitivity are core risk factors. The innovative introduction of reinforcement learning dynamic credit algorithm allows the credit limit to be adjusted in real time with coal price fluctuations, and the deployment of blockchain smart contracts enables automatic loan release after goods delivery. Empirical evidence shows that the system has increased the credit assessment F1 value to 91.2%, reduced the bad debt rate to 1.18%, and shortened the loan decision-making time to 2.1 hours, providing a new paradigm of data collaborative governance and intelligent decision-making for coal supply chain financial risk management.

Supply chains around worldwide are growing increasingly vulnerable to intricate and interrelated hazards, such as localized disruptions, ie. the COVID-19 pandemic—have repercussions that affect manufacturers, suppliers, and buying habits in other geographical areas. This study offers a risk-resilient paradigm for supply chain optimization that combines cutting-edge risk mitigation and recovery techniques with conventional efficiency-driven tactics. The framework tackles both short-term operational issues and long-term sustainability objectives by fusing lean management, scenario-based stochastic demonstrating, Bayesian network analysis, and digital technologies like blockchain and artificial intelligence. In order to reduce the spread of disruptions and enhance decision-making in the face of uncertainty, this work highlights the significance of supplier collaboration, decentralized planning, and predictive analytics. This paper includes a thorough strategy for managing the ripple effect, improving supply chain adaptation, and guaranteeing continuous value delivery in unstable circumstances using comparative analysis and data from recent literature.

This study examines the effects of the rising live streaming e-commerce on the 3DP supply chain, employing system dynamics to develop separate models for pure polymer and polymer-metal mixed printing. The analysis focuses on optimizing the 3DP supply chain configuration. Results indicate that, based solely on printing time, cost, and quality metrics, Corporate-live-3DP services are optimal for live commerce scenarios. However, despite this, Private-live-3DP maintains a substantial consumer base in practice, as evidenced by literature data and case studies. Both models pose significant challenges to conventional supply chains, necessitating adaptation. For Corporate-live-3DP, optimization strategies may include technology advancements, digital transformation, agile manufacturing, global network optimization, innovative management, collaborative R&D, fine-tuned inventory control, quality system upgrades, talent development, and organizational restructuring. Conversely, Private-live-3DP can be optimized through consolidation of private 3D printing resources, demand prediction and order optimization, supply chain collaboration platforms, quality management extensions, inventory strategy adjustments, increased transparency, regulatory compliance, and risk mitigation measures.

ABSTRACT: Supply chains are increasingly exposed to disruption, demand volatility, and operational complexity that traditional planning tools cannot fully capture in real time. Digital twin technology is emerging as a powerful capability for process simulation and optimization by creating a continuously updated virtual representation of supply chain operations. A digital twin combines physical process data, enterprise system transactions, and predictive analytics to simulate scenarios, evaluate trade-offs, and recommend optimal decisions before execution. This paper explores the role of digital twins in improving supply chain performance through simulation-based planning, bottleneck identification, inventory optimization, transportation network design, and resilience building. The study synthesizes research and industry frameworks to evaluate enabling technologies, implementation approaches, governance requirements, and barriers such as data quality, integration complexity, and organizational change. The findings indicate that digital twins can deliver measurable improvements in service levels, throughput, and cost efficiency by enabling decision-makers to evaluate outcomes under multiple conditions, including disruptions. However, value realization depends on strong master data, integration with ERP and IoT sources, and continuous model validation. The paper concludes with recommendations for implementing digital twins as an operational capability rather than a one-time transformation project.

The Digital Platform Services Supply Chain is an intricate ecosystem that faces challenges related to risk and sustainability, often relying on less interpretable statistical and heuristic models. This study introduces a Multi-Objective Morphological Analysis and Machine Learning based hybrid framework that incorporates Graph Neural Network, NeuroSymbolic Reasoning, and Reinforcement Learning to enhance decision-making in risk and sustainability assessments. The framework systematically evaluates various factors such as cyber risk, operational disruptions, and regulatory compliance while ensuring logical consistency and optimizing cloud selection strategies for resilience and sustainability. Stress testing confirms robustness in mitigating threats and disruptions, with results indicating improved adaptability, cost optimization, and alignment with the United Nations Sustainable Development Goals. Thus, this study contributes to hybrid intelligence-driven supply chain risk management, provides a scalable and interpretable decision-support system for sustainable digital platform service ecosystems.

No abstract available

As global environmental challenges intensify, the circular economy has emerged as a crucial pathway toward sustainable development. This study proposes a deep learning-based design framework for circular economy supply chain networks, aimed at optimizing resource allocation, reducing waste generation, and enhancing overall sustainability. The framework employs graph convolutional networks, long short-term memory networks, and multi-head attention mechanisms to construct a deep neural network model that effectively captures topological structural features, temporal dynamic information, and multi-dimensional correlations of supply chain networks. Through an improved NSGA-III multi-objective optimization algorithm, the framework achieves coordinated balance among multiple objectives including economic benefits, environmental protection, social responsibility, and circularity. A comprehensive sustainability evaluation index system provides quantitative assessment tools for circular economy supply chain performance. Experimental validation using real data from 15 enterprises across different industries demonstrates that the deep learning model achieves 89.2% prediction accuracy on the test set, representing a 16.1% improvement over baseline methods and 67.9% enhancement in computational efficiency. The optimized circular economy supply chain networks achieve 32.4% waste reduction, 28.7% resource efficiency improvement, and 25.3% cost reduction, with material circulation rate reaching 68.5% and network efficiency reaching 89.2%. This research provides theoretical foundation and practical guidance for manufacturing enterprises transitioning to circular economy, contributing significantly to achieving sustainable development goals.

Global supply chain volatility, critical material shortages and rising geopolitical risks pose severe challenges to the new energy vehicle manufacturing industry. Based on supply chain resilience theory, this paper analyzes the vulnerability nodes of the new energy vehicle industry supply chain and constructs a resili-ence enhancement framework covering supply network optimization, digital empowerment, diversified procurement and inventory management, green cy-cle system and policy coordination. The research shows that by enhancing the transparency of supply chain, strengthening the control of key links and estab-lishing agile response mechanism, the risk resistance and sustainable competi-tiveness of NEV supply chain can be significantly improved.

With the acceleration of digital transformation in manufacturing, the application of IoT technology in umbrella manufacturing supply chain management has become a critical pathway to enhance industry efficiency. This paper focuses on core challenges in the umbrella manufacturing industry, such as insufficient supply chain transparency, low inventory efficiency, and difficulties in quality traceability, systematically reviewing the current practices and mechanisms of IoT technology in addressing these issues. Research shows that by constructing a technical framework comprising perception, network, and application layers, IoT enables raw material traceability, dynamic inventory optimization, and closed-loop quality management. Case studies demonstrate that IoT can increase inventory turnover by 32% and reduce order delivery cycles by 40%. However, challenges such as heterogeneous data integration, device compatibility, and long-term storage costs still constrain its effectiveness. Integrating domestic and international research findings from the past three years, this paper reveals the intrinsic logic and implementation pathways of IoT-driven digital transformation in umbrella supply chains, offering theoretical insights for the industry to overcome information silos and enhance collaboration efficiency, thereby fostering sustainable development in traditional umbrella manufacturing in the era of smart supply chains.

Against the backdrop of intensified technological competition between China and the United States and prominent risks in the global semiconductor supply chain, enhancing supply chain resilience has become the key to the stable development of semiconductor enterprises. This paper takes Z Semiconductor Company as the research object, integrates supply chain resilience and complex network theory, and analyzes the problems faced by its supply chain, such as insufficient substitution of core nodes, low collaborative efficiency, and unbalanced layout. By constructing a research framework of theory - analysis - strategy, systematic improvement strategies are proposed from dimensions such as node diversification, network structure optimization, digital collaboration, global diversified layout and resilience guarantee system. The research provides practical references and management ideas for China's semiconductor manufacturing enterprises to deal with supply chain risks.

With the rapid development of the digital economy, enterprise supply chains are facing increasing complexity and uncertainty, creating an urgent need to enhance system resilience through intelligent technologies. This study aims to explore the application and underlying mechanisms of intelligent agents and generative artificial intelligence in enterprise supply chains. The research adopts a theoretical analysis approach, integrating resilience theory, dynamic capability theory, and complex network theory to examine how intelligent technologies enhance supply chain resilience through four mechanisms: information processing, collaborative optimization, risk response, and learning and evolution. The findings indicate that intelligent agents and generative artificial intelligence, through multidimensional synergy, not only strengthen supply chain information visualization, decision-making agility, and risk response capacity, but also achieve long-term adaptability through continuous learning and system optimization. The proposed integrative analytical framework reveals the logical relationships among these mechanisms, providing theoretical support for understanding the systemic role of intelligent technologies in improving supply chain resilience. The study concludes that the synergistic application of intelligent technologies can offer practical guidance for enterprises to build efficient, robust, and adaptive supply chain systems in the digital economy, while also providing a theoretical foundation for future empirical research and cross-industry technological applications.

This quantitative investigation delves into the complex dynamics of cold chain logistics, focusing on enhancing efficiency and resilience by strategically employing multi-source heterogeneous data, digital twins, and seamlessly integrating digital and real systems. By employing a structured questionnaire distributed among supply chain professionals and academic experts in China, encompassing key variables such as temperature control, digital twins, system integration, data standardization, and logistics network structure, the analysis seeks to comprehensively understand the interplay between these factors. Statistical analysis utilizing Statistical Package for Social Sciences (SPSS) and SmartPLS 3 software tools enables the identification of significant correlations and pathways between the variables under investigation. The findings shed light on several critical aspects of cold chain logistics optimization. The investigation revealed a positive association between enhanced temperature control measures and improvements in both operational efficiency and resilience within the logistics network. It demonstrates the efficacy of integrating digital twins in enhancing predictive analytics and mitigating risks effectively. The seamless integration of digital and real systems is found to expedite response times to disruptions, enhancing the overall agility of the logistics network. Furthermore, the study underscores the importance of data standardization efforts in promoting interoperability and collaboration among stakeholders. This study contributes to existing theoretical frameworks by integrating the Socio-Technical Systems Theory and the Resource-Based View. The practical implications of this study suggest that supply chain managers should leverage digital twins, data integration, and standardized processes to enhance temperature control, mitigate risks, improve visibility, and drive operational efficiency and resilience in cold chain logistics.

No abstract available

No abstract available

Aramco has established a corporate hydrocarbon quality program in 2015, undertaking a comprehensive, digitally enabled assessment of oil and gas facilities to identify challenges affecting hydrocarbon quality such as water and salt in crude and water in condensate., Crude Quality Solution first of its kind in the industry was developed to provide real-time visibility into crude quality across the entire supply chain with 50+ facilities and associated assets including pipelines, enabling proactive decision-making and optimized operations. The Crude Quality covers a complex network of crude oil streams processing five (5) crude grades Arab Super Light, Extra Light, Light, Medium and heavy. This digital solution providing end-to-end visibility and facilitating predictive maintenance, quality optimization, and operational excellence. A cutting-edge Crude Quality Solution was implemented as part of the value drivers at Fourth Industrial Revolution Center (4IRC), leveraging digitalization for crude quality compliance across all crude grades, aggregating over 15,000 data points from over 50 facilities, and providing visibility into hydrocarbon quality performance. This intelligent solution benchmarks compliance of all oil processing facilities against required crude quality specifications, facilitating a data-driven approach to crude quality management. The solution features a robust notification System, triggering instant alerts for any deviations from specified parameters, enabling prompt corrective actions, and mitigating potential impacts on crude quality. Key Performance Indicators (KPIs) have been established for each facility, allowing for precise measurement of overall performance and compliance, and triggering action from operation. In addition, multiple corporate assessments have been conducted to improve facility crude quality compliance across the company, yielding over 500 actionable recommendations ensuring sustainable compliance. The launch of the Crude Quality Program and the introduction of digitalization efforts have significantly improved crude quality compliance. Between 2015 and 2024, more than 40% operational improvement have been achieved in proactively maintaining crude quality compliance, showcasing the effectiveness of the program. Furthermore, several novel technologies have been introduced to support crude quality such as high efficiency mixers, vessel internals and level interface density profilers to sustain crude quality specifications The Crude Quality Program introduces a novel approach by demonstrating the synergistic effect of combining digitalization efforts, corporate technical assessments, and monitoring crude quality recommendations to achieve significant improvements in crude quality compliance. Valuable insights gained from this program offer opportunities for industry to leverage similar strategies, advancing the global energy excellence and leadership in hydrocarbon management.

Network strategy has evolved from periodic, cost-focused network redesigns into continuous, digital decision support as supply chains face rising volatility, disruptions, and complexity. Modern approaches leverage cloud-based digital models to evaluate both transformational and non-transformational decisions, including operations, risk, ESG, and capital investments. Leading organizations balance cost with resiliency, service, agility, and sustainability, redefining what “good” looks like end to end. Advances in AI, simulation, and blended optimization enable faster, repeatable, and implementable analysis at Stock Keeping Unit (SKU) level. However, success depends equally on new analytical skills, cross-functional alignment, and change management. This new approach requires how design teams may work with internal colleagues and external consulting partners. The value proposition for this new approach has never been higher. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

and analytics applications include logistics and supply chain control with real-time data, inventory control and management using sensing data, dynamic resource allocation in Industry 4.0 customized assembly systems, improving forecasting models using big data, machine learning techniques for process control, network visibility and risk control, optimizing systems based on predictive information (e.g., predictive maintenance), combining optimization and machine learning algorithms, and supply chain risk analytics.

With the integration of Artificial Intelligence (AI) along with Industrial Internet of Things (IIoT), industrial operations have transformed into a more efficient domain, where resource management, automated process monitoring, anomaly detection, etc., have taken a new form. With Industries 4.0 pushing many industries towards smart manufacturing and digital transformation, data-driven decision making, real-time optimization, and predictive maintenance has been made possible by various AI-enabled IIoT solutions. The study investigates how AI is used to optimize resource allocation, energy efficiency, and supply chain management using machine learning algorithms and predictive analytics. Besides, advanced AI-powered automated process monitoring systems use deep learning techniques and sensor networks together with the digital twin technology to detect bottlenecks, inefficiencies, failures, and wastages to optimize production workflows. AI-powered anomaly detection systems such as unsupervised learning models and tension neural networks are also used to improve alarm detection and cybersecurity in industry. Edge computing plays a crucial role in reducing data latency and improving system responsiveness by providing decentralized data processing closer to the source and running a real-time AI model. Though AI-powered IIoT solutions come with plenty of advantages, this needs to be balanced against data security, interpretability of models and computational scalability to achieve maximum efficiency in the industrial sector. Federated Learning, Quantum and Explainable AI will shape the future of AI in industrial automation for sustainable and intelligent industrial operations.

Having solved the data integration problem, we discuss how convergence of 4 technology vectors, namely Big Data, Artificial Intelligence, Cloud Computing, and Internet of Things (IoT) has, for the first time, enabled us to solve a class of problems previously deemed as unsolvable at massive scales. AI applications such as predictive maintenance, fraud detection, sensor network health, supply chain optimization, energy management, anti-money laundering, and customer engagement are among the set of problems that are now solvable at enterprise scale. This is possible, thanks to the platform that brings together all infrastructure, micro services, data sources and research and data scientist on the same platform, and in doing so improves the productivity of the development team by a factor of 10-100x. We will discuss the "Predictive Maintenance" problem applied to distribution networks, aircraft systems and oil and gas assets, "Inventory Optimization" problem solved in the manufacturing industry, and "Fraud Detection" in the electricity space as well as banking space.

The promise of bio-based polymers like polylactic acid (PLA) and polyhydroxyalkanoates (PHA) as sustainable packaging solutions is tempered by significant real-world challenges: persistent cost premiums, complex supply chain logistics, and geographically variable environmental footprints that diminish their theoretical benefits. This study directly addresses these complexities by combining cradle-to-grave Life Cycle Assessment (ISO 14040/44) with a multi-objective Mixed-Integer Linear Programming (MILP) model to identify economically feasible and environmentally superior configurations for regional PLA and PHA packaging systems across Western Europe. Our innovative approach goes beyond traditional methods by optimizing facility locations, transportation networks, feedstock sourcing, and end-of-life options simultaneously while assessing trade-offs between total costs and Global Warming Potential (GWP). The analysis offers valuable insights: PHA consistently outperforms PLA in net energy demand (-6.5 to +9.6 MJ/kg) and achieves true circularity within effective composting systems, yet requires significant policy support to overcome its 20% capital cost disadvantage. While localized production significantly reduces emissions by 15–30% in regions rich in renewable energy, it also increases costs by 10–15%—a paradox that can only be managed through strategically designed policies. Targeted incentives for composting infrastructure further boost circularity metrics by 40%, turning waste streams into valuable resources. These findings provide policymakers with a rigorous, data-driven framework to guide regional bio-polymer transitions, shifting from generic mandates to precise strategies that promote sustainability. Ultimately, this research shows that the sustainable packaging revolution depends not just on materials innovation but also on strategically coordinating regional advantages and technological potential. Keywords: Life Cycle Assessment, Supply Chain Optimization, Bio-based Polymers, Polyhydroxyalkanoates (PHA), Polylactic Acid (PLA), Circular Economy, Regional Sustainability, Territorial Symbiosis, Packaging Systems, Policy Integration.

This study investigates the efficient strategies for supply chain network optimization, specifically aimed at reducing industrial carbon emissions. Amidst escalating concerns about global climate change, industry sectors are motivated to counteract the negative environmental implications of their supply chain networks. This paper introduces a novel framework for optimizing these networks via strategic approaches which lead to a definitive decrease in carbon emissions. We introduce Adaptive Carbon Emissions Indexing (ACEI), utilizing real-time carbon emissions data to drive instantaneous adjustments in supply chain operations. This adaptability predicates on evolving environmental regulations, fluctuating market trends and emerging technological advancements. The empirical validations demonstrate our strategy's effectiveness in various industrial sectors, indicating a significant reduction in carbon emissions and an increase in operational efficiency. This method also evidences resilience in the face of sudden disruptions and crises, reflecting its robustness.

No abstract available

No abstract available

Supply chain efficiency, transparency, and sustainability can be enhanced using blockchain technology. Blockchain enables a company to accurately track raw material origin to finished products, which ensures standard quality and sustainability. Furthermore, blockchain improves partnerships between supply chain stakeholders by providing a confident, common platform for sharing data. The influence of implementing blockchain technology on supply chain sustainability includes reduced wastage, increased resource transparency, monitoring of social standards, and reduced operational costs. The challenges of blockchain implementation include scalability, incorporation with present systems, lack of expertise, safety and confidentiality, and regulatory uncertainty. Companies can tackle these challenges through collaborative approaches and technical improvements. In general, blockchain technology significantly enhances supply chain sustainability and efficiency, which provides opportunities for creative business solutions.

Supply chain management depends on a complex, interconnected network of suppliers, manufacturers, transportation companies, distributors and customers with the goal of predicting, monitoring and controlling operations and processes. Globalization has introduced fierce competition, forcing supply chains to innovate and enhance their performance and capabilities. Centralized management systems are prone to attacks, disruptions and malfunctions. A potential solution to these known issues is the adoption of blockchain technology. The blockchain offers an immutable ledger that allows for a trustless, decentralized system without reliance on third parties. It can provide new features, improve performance, advance network visibility and strengthen the four flows of a supply chain. To survive and compete on the global stage, supply chains must adopt emergent technologies to develop new business strategies. In this paper, a comprehensive survey of academic literature and research works relating to blockchain platforms for global supply chain management is presented. This survey will provide an overview of blockchain technology for supply chain management, summarize industry applications, highlight persistent challenges, and identify research opportunities to enhance the current state of research in the past six years. The contribution of this survey is to also provide a list of available blockchain solutions for global supply chain management and to elaborate on future advancements in the field. New solutions will be proposed and explained.

As supply chains become increasingly digitized and decentralized, ensuring security, traceability, and data integrity has emerged as a critical concern. Blockchain technology has shown significant potential to address these challenges by providing immutable records, transparent data flows, and tamper-resistant transaction logs. However, the effective application of blockchain in real-world supply chains requires the careful evaluation of both architectural design and technical limitations, including scalability, interoperability, and privacy. This review systematically examines existing blockchain-based supply chain solutions, classifying them based on their structural models, cryptographic foundations, and storage strategies. Special attention is also given to underexplored humanitarian logistics scenarios. It introduces a three-dimensional evaluation framework to assess security, traceability, and integrity across different architectural approaches. In doing so, it explores key technological enablers, including advanced mechanisms such as zero-knowledge proofs (ZKPs) and cross-chain architectures, to meet evolving privacy and interoperability demands. Furthermore, this study outlines a conceptual cross-chain interaction scenario involving permissioned and permissionless blockchain networks, connected through a bridge mechanism and supported by representative smart contract logic. The model illustrates how decentralized stakeholders can interact securely across heterogeneous blockchain platforms. By integrating quantitative metrics, architectural simulations, and qualitative analyses, this paper contributes to a deeper understanding of blockchain’s role in next-generation supply chains, offering guidance for researchers and practitioners aiming to design resilient and trustworthy supply chain management (SCM) systems.

This study aims to empirically investigate the effect of blockchain technology (BCT) adoption on supply chain resilience (SCR), with the mediating role of supply chain integration (SCI) and the crucial effect of environmental dynamism (ED) as a moderator. Based on data collected from firms operating in the automotive industry in India, the proposed model was tested using Partial Least Squares Structural Equations Modelling (PLS-SEM) via SmartPLS software. The empirical results showed a positive effect of BCT on SCI, which in turn affects SCR. Importantly, SCI acts as a full mediator in the BCT-SCR relationship, which is moderated by ED, that is, the effect of BCT on SCR via SCI is strong when ED is high. This study offers the groundwork for operationalizing BCT in a supply chain context. It also contributes to SCR research by investigating how SCI mediates the effect of BCT on SCR. In addition, this study found a moderating effect of ED on the relationship between BCT and SCI. These results provide insights to auto manufacturers on ways to enhance SCR and ensure safe supply chain operations.

Highlights A scoping review of 385 articles containing relevant literature, research studies, and connections among postharvest losses, produce quality, supply chain management, and technology use was performed. Several postharvest factors, including handling, pre-cooling, washing and sanitation, sorting and grading, packaging, storage, and transport, play key roles in maintaining produce quality, but stakeholders in developing countries or emerging economies often lack access, infrastructure, equipment, and capital, resulting in significant losses. Supply chain management plays an important role in reducing postharvest losses, yet managerial, socioeconomic, and supply hindrances create impediments to fresh food industries in developing countries. Recently developed technologies in cold storage, artificial intelligence, and tracking and tracing can have a significant impact on produce quality, and while developing countries have adopted new technologies rapidly, there are still financial, social, and regulatory obstacles limiting uptake. Abstract. Rising global population and increased demand for fresh food, especially in developing countries, underscore the critical need to address postharvest losses to ensure food security. Effective postharvest practices, supply chain management, and technology are key factors to sustaining produce quality. However, the magnitude and nature of these factors vary between developed and developing countries. Developed countries tend to have more efficient supply chain management systems, a highly skilled workforce, and greater access to advanced technologies, leading to a higher level of produce quality and fewer postharvest losses. In contrast, developing countries often suffer from inadequate infrastructure, a lack of access to modern technologies, a lack of skills to properly grade, sort, package, transport, and store produce, as well as poor quality control. Addressing these issues through improved supply chain management, the adoption of appropriate technologies, and training and education could lead to a reduction in postharvest losses, produce quality that is maintained, and a safer, more secure food supply. This article presents a scoping review of literature on postharvest factors, technologies, and practices resulting in the deterioration and losses of fruits and vegetables in fresh food supply chains, with an emphasis on developing countries. Existing and emerging technologies and supply chain management practices that maintain quality and facilitate the delivery of high-quality produce were highlighted. First, a conceptual framework for supply chains is presented to guide discussion on technical, economic, and policy opportunities and to provide insights that may strengthen the resilience of food systems in developing countries. Next, techniques and practices used in the produce industry were summarized, including how effective supply chain management can maintain quality and reduce postharvest losses at key distribution stages. Finally, recent literature on technology, applications, and barriers to adoption is presented, and strategies to strengthen supply chain resilience in developing countries are discussed. Keywords: Developing countries, Food loss, Food security, Postharvest losses, Postharvest management, Produce quality, Smallholder farmers, Supply chain management, Technology adoption.

No abstract available

Supply chain management (SCM) plays an important role in organizations by creating efficiency and cost advantages in operating supply chain activities across industries. It will also be important to know that traditional SCM systems have various inherent problems, including not having enough information about the system, complicated tracking of products, and the fact that fraudulent activities easily compromise most systems. The modern solution for SCM seems to be based on blockchain technology that provides an operational walls-built ledger, significantly increasing transparency and traceability rates within the supply chain processes. Consequently, this paper considers the role of SCM in incorporating blockchain technology to address potential issues such as scalability and conformity to legal frameworks. Examples from Walmart's food supply chain management and Maersk and IBM's TradeLens give a concrete realization of blockchain's benefits in supply chain clarity and functioning. Future trends of blockchain for SCM, particularly multichain and integration with other emerging technologies, reveal that the field is set to expand toward offering increased resolution and reliability to the supply chain.

Abstract Trust, traceability, and transparency emerge as critical factors in designing circular blockchain platforms in supply chains. To bridge the three circular supply chain reverse processes (i.e., recycle, redistribute, remanufacture) and the three factors affecting blockchain technologies (i.e., trust, traceability, transparency), this paper proposes the integrated Triple Retry framework for designing circular blockchain platforms. A circular blockchain platform was designed in a supply chain, including manufacturer, reverse logistics service provider, selection center, recycling center, and landfill. The results highlight blockchain's role as a technological capability for improving control in the movement of wastes and product return management activities.

Highlights • Evaluate 178 research articles in the field of blockchain implementation in supply chains.• Identify the current trends of research on blockchain in different domains of supply chain operations.• Examine various supply chain functions that can be enhanced through blockchain technology.• Present strong excerpts of blockchain applications in supply chains across various industrial sectors.• Suggest managerial implications, highlight challenges and build a future research agenda.

Blockchain technology has emerged as a promising solution to enhance supply chain transparency and sustainability in the construction industry. However, the widespread adoption of blockchain faces several barriers that need to be identified and understood. The construction industry faces significant challenges regarding supply chain transparency and sustainability. Current practices lack visibility, leading to difficulties in tracing material origins, tracking movement, and ensuring compliance. To fill this gap, this study employed a three-phase approach. In the first phase, a comprehensive literature review identified 37 potential barriers. Subsequently, expert discussions were held to refine the list, ultimately selecting 15 barriers of utmost importance. In the second phase, data were collected from 17 experts representing academia and industry. Finally, in the last phase, the collected data were analyzed using the Pythagorean fuzzy analytical hierarchical process (AHP) methodology. The findings revealed that the “transparency range” category was the most critical barrier, closely followed by “inadequate access to institutional finance”. Surprisingly, the study identified the “security environment” as the most significant barrier. These results offer construction companies, policymakers, and other industry stakeholders a comprehensive understanding of blockchain adoption’s challenges. With this knowledge, stakeholders can design effective strategies and policies to address these barriers. Moreover, the research highlights the importance of considering uncertainty in decision making when assessing technology adoption, making the findings applicable beyond the construction industry.

No abstract available

Increasing complexity and the involvement of additional stakeholders make it impossible to predict the impact of each decision, which puts supply chain managers in uncertain situations. However, a supply chain that can adapt and react to the current scenario gives them some control over these ambiguous circumstances. These characteristics of sensing disturbances or threats and giving appropriate responses can be improved with the implementation of blockchain‐enabled technologies and can prove critical to the success of supply chain resilience and sustainability. This study has identified 21 blockchain technology‐enabled critical success factors for supply chain resilience and sustainability and grey theory is used to address the limitation of data availability. This study incorporates the combination of the Grey‐DEMATEL (Decision Making Trial and Evaluation Laboratory) method to investigate the impact of critical success factors and to obtain the cause/effect relationship. Sensitivity analysis is performed to assess the robustness of obtained results. The findings indicate that internal integration is the most crucial causal factor, as it initiates the effects of many other critical success factors. Whereas Standardized Data Management, followed by Smart Ordering tops the effect group. As blockchain technology is still in its early stages of development, this study will encourage researchers and industry practitioners to strive for greater efficiency and effectiveness in their supply chain practices and to enhance the resilience and sustainability of their supply chains.

Supply chain resilience is on the agenda of academia and industry like never before. One strong instigator for this phenomenon has been the COVID-19 pandemic, which opened the era of global uncertainties and vulnerabilities. In this paper, we analyse the transformation of supply chain resilience research through the COVID-19 pandemic. Methodologically, we use a hybrid approach based on a combination of elements of a bibliometric and expert analysis to compare the main topics of resilience research before, during, and after the pandemic. Along with an expected observation about an exponential growth of literature on supply chain resilience in and after 2020, we observe a major shift from preparedness and disruption predictions in the pre-pandemic literature towards recovery and proactive adaptation in the pandemic and post-pandemic research. Our analysis systematically reveals some new topics, management practices, and future research areas in supply chain resilience. In particular, digital technology, supply chain viability, the cross-industry ripple effect, and intertwined networks have become new and impactful research areas during the COVID-19 pandemic. Further developments of these topics are expected to be continued in future. Managerial and theoretical implications of the said developments conclude this paper.

No abstract available

Editor’s notes: Field-programmable gate array (FPGA) bitstream reverse engineering and counterfeiting is a pertinent challenge in the modern hardware supply chain. To this end, this article proposes a blockchain-based technology to foster authenticity and integrity of the FPGA supply chain for trustworthy traceability. The proposed approach is transformative in being able to detect counterfeit FPGA chips and bitstreams using state-of-the-art blockchain technologies.—Kanad Basu, The University of Texas at Dallas

Blockchain technology is a major innovation that has swept through global supply chains recently. Blockchain technology has received immense attention in the supply chain industry due to its promising capabilities. This study was conducted to evaluate the potential capabilities of blockchain technology, which are highly relevant to the supply chain industry. To improve the understanding of the effect of blockchain on the supply chain, this research focuses on two crucial aspects of supply chain management, namely, supply chain capabilities and flexibility. The research procures measuring items for blockchain characteristics, supply chain capabilities, and flexibility through a questionnaire, the previous literature, and interviews conducted with industry experts. Through the use of statistical analysis, this study identifies the relationship between the above variables. The effect of blockchain on each variable is examined using a simple linear regression model. The findings disclose that blockchain technology has generated a notable impact on the supply chain capabilities and supply chain flexibility of firms. This makes blockchain technology highly essential for firms to generate a competitive advantage in the market and develop a new set of capabilities ahead of their competitors.

No abstract available

The study investigates the relationship between the information and communication-enabled supply chain integration (SCI) and sustainable supply chain performance (SSCP). Moreover, to the best of our knowledge, there is no empirical evidence on the impact of blockchain technologies (BT) on the SSCP. Therefore, the primary aim of this study is to assess the relationship between BT and SSCP. More specifically, the study was conducted to examine the direct influence of BT on SCI and SSCP and the interactive effect of BT and SCI on SSCP. Based on the dynamic capability theoretical lens, the present study conceptualizes the use of BT as a specific IT resource to collaborate and reconfigure the ties with the upstream and downstream supply chain members to achieve SSCP. The results of the study support the hypothesis stating that BT positively influences the SSCP. The results recognize the role of SCI as a significant mediating variable between the BT and SSCP. The result indicates the strong influence of SCI with full mediation effect on the relationship between the BT and SSCP.

Supply chain management (SCM) is a core corporate activity responsible for moving commodities and services from one point to another through a variety of stakeholders. The traditional SCM is based on a centralized approach managed at the central headquarter, and all other sub-offices get instructions from the main office. Some major issues with present SCM systems are security, transactional transparency, traceability, stakeholder involvement, product counterfeiting, additional delays, fraud, and instabilities. Blockchain (BC) emerges as a technology that can manage the data and build trust efficiently and transparently. It can also aid in transaction authorization and verification in the supply chain or payments without a third party. To address the present SCM issues, BC technology is a feasible solution. Motivated by the aforementioned considerations, in this paper, we present a survey on the adoption of BC in SCM. This paper undertakes a comprehensive analysis of the literature on BC characteristics, implementations, and business consequences in various SCM. This Blockchain-centered study, in particular, discloses the research state and delineates future research directions by studying and analyzing 97 up-to-date publications highlighting BC’s supply chain uses. Transparency and traceability, information sharing, product anti-counterfeiting, and building trust are the major aspects propelling BC’s implementation in SCM. Further, we analyzed various applications of SCM in which BC can be used as a probable technology to secure all transactions. Then, we have highlighted open issues and research challenges for adopting BC technology in SCM that open the doors for beginners eager to start work in this amazing area.

This paper offers a comprehensive conceptual review of modern supply chain management (SCM), placing emphasis on its theoretical progression and the integration of advanced digital technologies. The review encompasses the classical SCM cycle (planning, sourcing, manufacturing, delivery, returns), modern digital enablers (IoT, blockchain, ERP, big data analytics), strategic models (Lean, Agile, Responsive, JIT, VMI, Omni-channel), and human competencies essential for successful implementation. Through critical synthesis, the paper highlights how digitization enhances visibility, agility, and resilience, while also uncovering inherent challenges such as complexity and ethical concerns. The outcome is a robust knowledge foundation to inform future empirical studies and to guide the design of digitally-enabled, sustainable supply networks in diverse business contexts.

Mergers in the automotive sector are frequently motivated by the potential for cost synergies, market share enhancement, and operational efficiency. The supply chain is a crucial domain that experiences immediate effects during post-merger integration (PMI). This research examines supply chain efficiency improvements in the automobile manufacturing sector during post-merger integration (PMI), using Stellantis’ formation from Fiat Chrysler Automobiles (FCA) and Peugeot S.A. (PSA) as a primary case study. Comparative analyses of ZF Friedrichshafen-WABCO, Geely-BYD, and BMW’s Industry 4.0 adoption illustrate the transformative role of IoT and AI in enhancing real-time visibility, predictive analytics, and cost efficiency. However, challenges like Stellantis’ 2024 Ram truck recall underscore the need for robust digital risk management and diversified supplier networks. Key findings of the study highlight a 22.7% increase in inventory turnover, a 17.4% reduction in lead times, and a 12.5% decrease in procurement costs per vehicle, driven by supplier consolidation, ERP standardization, and logistics optimization. A predictive model for the Nissan-Honda- Mitsubishi 2025 merger further explores synergies in EV technology, market alignment, and Industry 4.0 integration, emphasizing leadership commitment and cultural cohesion as critical factors. By combining quantitative insights with actionable strategies, this study offers a comprehensive framework for achieving operational excellence and competitive advantage in post-merger scenarios, providing valuable guidance for automakers to navigate the PMI challenges.

PurposeDigital transformation (DT) in the semiconductor industry goes beyond traditional business operations and supply chain management (OSCM) to the digital world. Despite significant developments in recent years, blockchain implementations for OSCM remain relatively underdeveloped in the semiconductor industry. Therefore, this research aims to examine the relationships between blockchain visibility, supply chain integration (SCI) and supply chain performance (SCP) in the era of DT in Malaysia's semiconductor industry to shed light on this emerging area.Design/methodology/approachA convenience sampling of 71 operations and supply chain managers attached to semiconductor manufacturing firms in Malaysia were invited to participate in a survey. In assessing blockchain visibility within the industry, key terms namely business intelligence gathering, information exchange, information technology (IT) and knowledge of asset status, were conceptualised from the literature review. The questionnaires developed to collect data were validated by industry and academic experts.FindingsThe results from the analysis confirmed that SCI mediates the link between blockchain visibility (information exchange, business intelligence gathering and knowledge asset status) and SCP. Likewise, the importance-performance matrix analysis (IPMA) outcomes revealed that IT played a minor role. The results suggested that semiconductor manufacturers should pay less attention to IT since this was identified as having the least priority towards improvement.Practical implicationsThe outcomes from this research enable policymakers to strategise and integrate blockchain technology in the era of DT to ensure sustainable SCM in the semiconductor industry in Malaysia.Originality/valueThe research bridge the knowledge gap by revealing the value that blockchain visibility can facilitate SCP and explore SCI as the prevailing factor and demonstrates how Resource-Based Theory and Network Theory can be applied in this study.

This study investigates the role of blockchain technology (BCT) in enhancing supply chain (SC) efficiency and performance, focusing on its interaction with digital transformation-enabled dynamic capabilities (DTeDC). Using a qualitative abductive methodology, semi-structured interviews with 31 industry experts were conducted. Thematic analysis of the data revealed the mechanisms through which BCT enhances SC performance. A conceptual model was developed, and five propositions were proposed, grounded in the dynamic capability view. The empirical findings reveal that DTeDC positively influences both operational and strategic efficiency within the SC, which in turn leads to enhanced SC performance. The study elucidates the complex interactions between BCT and DTeDC, highlighting how these dynamic capabilities facilitate the digital transformation of SCs, thereby improving their efficiency and performance. This research makes a significant contribution to the existing literature by advancing our understanding of the integration of BCT in SCs. It addresses prevailing inconsistencies regarding the applicability and effectiveness of BCT, offering new insights into its role in SC efficiency and performance.

This study explores how digital twin technology boosts operational efficiency within supply chains. It identifies key attributes influencing logistics and performance, synthesizing existing research insights and highlights knowledge gaps to guide organizations considering digital twin adoption. The research involved a systematic literature review, focusing on articles from 2014 to 2024, sourced from the Science Direct database. Keywords related to digital twins and supply chain management informed the selection criteria. Findings reveal significant benefits from digital twin integration, including process optimization, error reduction and shorter production cycles (15–25%). Energy consumption and downtime decrease by 15–20%, while productivity improves by up to 25%. Additionally, cost savings of up to 30% are achieved, along with enhanced flexibility, responsiveness to market changes and resilience to disruptions. Collaboration among stakeholders within the supply chain is also strengthened. This research positions digital twins as a strategic tool for supply chain optimization. It offers practitioners and researchers a robust conceptual foundation to assess their impact on operational efficiency and paves the way for further exploration in this transformative area.

As supply chains grow increasingly complex, organizations are turning to Digital Twin Technology, to enhance project management success. This study explores the role of RealTime Data Integration (RTD) and Simulation & Predictive Analytics (SPA), in optimizing supply chain project execution. By leveraging real-time data, organizations can improve visibility, risk mitigation and operational efficiency. Simulation and predictive analytics enable scenario planning and data-driven decision-making. A quantitative approach was employed to analyze the impact of these technologies on project outcomes. The findings revealed that both RTD and SPA significantly contributed, to project management success, with SPA displaying a stronger influence. The study highlights the critical role of digital twin technology in improving forecasting, resource allocation and collaboration, ultimately enhancing project efficiency. These insights could provide valuable recommendations for supply chain professionals and project managers, seeking to leverage digital twins for strategic decision-making and risk management. Future research can explore advanced AI-driven models and industry-specific applications of digital twin technology, to further optimize supply chain project management.

Digital supply chain integration is becoming increasingly dynamic. Access to customer demand needs to be shared effectively, and product and service deliveries must be tracked to provide visibility in the supply chain. Business process integration is based on standards and reference architectures, which should offer end-to-end integration of product data. Companies operating in supply chains establish process and data integration through the specialized intermediate companies, whose role is to establish interoperability by mapping and integrating companyspecific data for various organizations and systems. This has typically caused high integration costs, and diffusion is slow. This paper investigates the requirements and functionalities of supply chain integration. Cloud integration can be expected to offer a cost-effective business model for interoperable digital supply chains. We explain how supply chain integration through the blockchain technology can achieve disruptive transformation in digital supply chains and networks.

跨流域调水工程运营中往往缺乏有效的合作运营机制，导致参与主体与工程整体的运营目标相偏离。论文从非合作博弈视角和合作博弈视角，分别构建了不同联盟组合下跨流域调水供应链非合作博弈模型、基于核仁、弱核仁、比例核仁方法和Shapley值方法的跨流域调水供应链合作博弈模型，并进行了对比数值分析，在此基础上，探讨了跨流域调水供应链合作运营机制。研究结果表明：(1) 基于核仁、弱核仁、比例核仁方法和Shapley值方法的合作博弈下跨流域调水供应链及其成员的运营绩效均不低于非合作博弈情形，实现了参与各方利益的帕累托改进。(2) 由于非合作博弈下系统供应商的“先动优势”，基于核仁、弱核仁、比例核仁方法和Shapley值方法的利益分配方案下，系统供应商获得的利润高于本地分销商和外地分销商。(3) 基于比例核仁方法的供应链利益分配方案下本地分销商的利润并没有改善，该方案下的合作联盟不稳定。(4) 基于核仁方法和弱核仁方法的供应链利益分配方案体现的是更加平均主义的思想，为跨流域调水工程提供了一种更为公平的合作运营机制；而基于Shapley值方法的供应链利益分配方案体现的是功利主义原则，为跨流域调水工程提供了一种更为高效的合作运营机制。 The operation management of inter-basin water diversion (IBWT) project is usually lack of efficient cooperative operations mechanism, which leads to the bias between the operation goal of the whole project and the operations goal of the participants. From both the perspectives of non-cooperative game and cooperative game, non-cooperative game models under different coalition combination and cooperative game models based on methods of nucleolus weak nucleolus, proportional nucleolus and Shapley value for the IBWT supply chain are developed, respectively. The corresponding numerical analysis is implemented, and the cooperative operations mechanism for the IBWT supply chain is also discussed in this article. This study suggests that: i) the operations performance of the IBWT supply chain and its members under the cooperative game based on the methods of nucleolus, weak nucleolus, proportional nucleolus and Shapley value, are no less than that under the non-cooperative game, and participant’s benefits achieve Pareto improvement. ii) Due to the water supplier’s advantage of “first-move”, water supplier gains more profit than the local distributor and the external distributor under the profits allocation scheme based on the methods of nucleolus, weak nucleolus, proportional nucleolus and Shapley value. iii) The local distributor’s profit is not improved under the profits allocation scheme based on the proportional nucleolus method, and the cooperative alliance is not stale under this allocation scheme. iv) The profits allocation scheme based on the methods of nucleolus and weak nucleolus shows the thinking of equalitarianism, providing a more equitable cooperative-operation mechanism for the IBWT project; the profits allocation scheme based on the method of Shapley shows the principle of utilitarianism, providing a more efficient cooperative-operation mechanism for the IBWT project.

No abstract available

No abstract available

No abstract available

No abstract available

No abstract available

No abstract available

No abstract available

No abstract available

No abstract available

This study focuses on the application of immersive media in the digital transformation of cultural heritage, taking the “Return to Fengyuan” project in Fengyuan Ancient Village as a case. It explores the practical integration of AIGC and MR technologies in shaping ancient architectural spaces. Based on a semiotic framework, the research constructs a method for transforming architectural symbols into digital experiences, integrating point cloud scanning, BIM modeling, AI-assisted generation, and mixed reality technologies to establish a human–machine collaborative design workflow. The study reveals the mechanisms by which digital technologies reshape traditional cultural knowledge production, memory construction, and audience participation models, providing a theoretical framework and methodological reference for the digital revitalization of cultural heritage.

No abstract available

No abstract available

Automatic equipment and information technologies make it possible to acquire multi-scale and multi-source heterogeneous data of crops under different growth conditions, forming big data on crop phenomics. This will greatly promote the research progress of crop functional genomics, digital breeding, and smart cultivation. In this paper, the demand for and industrial development of technology and equipment of big data on crop phenomics are analyzed. Then, the current situation of research and development in this area is summarized from five aspects: data acquisition hardware, data transmission, data analysis, knowledge formation, and applications. The problems and developmental trends of relevant technologies, equipment, and industrial application in China are analyzed from the perspectives of high-throughput acquisition and intelligent analysis of big data on crop phenomics. At last, the following suggestions are proposed: achieving breakthroughs regarding key crop phenotyping sensor technologies from the underlying chip level, forming an autonomous phenotyping extraction technology system on the basis of controllable open source, 收稿日期:2023-04-21;修回日期:2023-06-12 通讯作者:赵春江,北京市农林科学院信息技术研究中心研究员,中国工程院院士,研究方向为农业信息技术与智能装备; E-mail: zhaocj@nercita.org.cn 资助项目:国家重点研发计划项目(2022YFD2002300);中国工程院咨询项目“生物育种数字化发展战略研究”(2021-JJZD-04),“安徽省智 慧农业发展战略研究”(2021-DFZ-17) 本刊网址:www.engineering.org.cn/ch/journal/sscae