Letters of Credit (LCs) are pivotal in global trade finance, yet traditional processes are plagued by inefficiencies, fraud, and a lack of transparency, particularly in developing economies such as Bangladesh. This study investigates how three technological innovations Blockchain Database Integration (BDI), Collaborative Platforms (COP), and Compliance Automation (CAU) drive the evolution of a Sustainable Letters of Credit Supply Chain (SLCSC), mediated through the development of a Technology-based LC Supply Chain (LCSC). Drawing on a sample of 400 LC stakeholders in Bangladesh, the research employed a quantitative methodology using a reflective measurement model. Data were analyzed using Exploratory Factor Analysis (EFA) in IBM SPSS and Structural Equation Modeling (SEM) in IBM AMOS to assess reliability, validity, and the hypothesized relationships. The results indicate that Compliance Automation exerts a strong, significant positive effect on the LCSC (? = 0.661, p < 0.001) and Collaborative Platforms a weaker but significant effect (? = 0.087, p = 0.04), whereas Blockchain Database Integration has no significant effect (? = 0.016, p = 0.687). The LCSC, in turn, exerts a strong positive impact on the SLCSC (? = 0.938, p < 0.001). The findings demonstrate that compliance automation is the primary enabler of a technology-based LC supply chain, while the influence of blockchain remains constrained by prevailing infrastructural and regulatory conditions. The study’s principal contribution is to disaggregate the technological drivers of trade-finance digitalization into three empirically distinct constructs and to demonstrate that their influence on sustainability is fully mediated by the technology-based LC supply chain, providing structural- model evidence of this mechanism from Bangladesh’s banking sector. The study offers critical insights for banks, businesses, and policymakers seeking to modernize LC operations for enhanced efficiency, security, and eco- efficiency.
Марат Ібатуллін, O. Vasylenko, Iryna Zakryzhevska, Віталій Карпенко
The article examines current trends in the adaptation of international food trade to the requirements of the European Union and global food safety standards under conditions of increasing competition, digitalization of trade processes, and transformation of global agri-food markets. It has been determined that Ukraine’s integration into the European economic area is accompanied by the need to harmonize national legislation with EU sanitary, phytosanitary, and technical regulations, improve food quality control systems, and strengthen the institutional framework of international trade. It is substantiated that compliance with international food safety standards is becoming a key prerequisite for enhancing the competitiveness of agri-food products in foreign markets and expanding Ukraine’s export potential. The impact of non-tariff barriers, certification procedures, and product traceability requirements on the functioning of international food trade is analyzed. It is established that the modern model of international agri-food trade is shaped by stricter requirements for environmental responsibility of producers, digital traceability of supply chains, implementation of ESG-oriented approaches, and the development of electronic certification systems. Particular attention is paid to the role of digitalization of trade procedures, electronic document management, blockchain technologies, and digital platforms in ensuring transparency of international supply chains, reducing administrative costs for enterprises, and accelerating customs clearance procedures for export operations. It has been proved that the adaptation of international food trade to global food safety standards requires comprehensive modernization of the state regulatory system, development of laboratory and certification infrastructure, improvement of customs and logistics procedures, and expansion of digital integration among agri-food market participants. It has been determined that important directions for increasing the efficiency of international trade include harmonization of the regulatory framework with international requirements, implementation of risk-based control mechanisms, support for exports of value-added products, and development of digital food traceability systems. It is substantiated that the implementation of the proposed measures will contribute to increasing the competitiveness of Ukrainian agri-food products, strengthening food security, expanding access to international markets, and forming a modern model of international food trade in accordance with EU requirements and global food safety standards.
Background Scientific output on digital transformation in healthcare and pharmaceutical supply chains increased substantially after 2020, indicating growing research attention to resilient and digitally integrated logistics systems. However, the literature remains fragmented across technologies such as blockchain, artificial intelligence, Internet of Things, predictive analytics, cold chain monitoring and healthcare logistics optimization. Methods This study conducted a bibliometric analysis of scientific publications related to digital transformation and emerging technologies in healthcare and pharmaceutical supply chains. Data were retrieved from Scopus, PubMed and Web of Science databases following PRISMA 2020 screening principles. After duplicate removal and eligibility assessment, 83 peer-reviewed English-language journal articles published between 2015 and 2026 were included in the final analysis. Bibliometric mapping and thematic analysis were performed using VOSviewer and Bibliometrix/Biblioshiny. Results Within the analyzed corpus, the results showed a substantial increase in scientific publications after 2020, consistent with growing research attention to resilient and digitally integrated healthcare supply chains. Blockchain showed the highest visibility in keyword and citation-based analyses, particularly in relation to traceability, transparency and anti-counterfeit systems. Additional major research areas included artificial intelligence, predictive analytics, IoT-based cold chain monitoring and healthcare logistics optimization. Thematic analysis identified strong literature-based associations between digital technologies, supply chain resilience and pharmaceutical traceability systems. Conclusions Digital technologies are increasingly represented in research on healthcare and pharmaceutical supply chain transformation. The findings suggest that blockchain, AI and IoT technologies may support transparency, traceability and operational resilience. However, implementation barriers related to interoperability, infrastructure costs, data privacy and regulatory complexity remain significant challenges. These findings should be interpreted as bibliometric and thematic patterns within the analyzed English-language journal literature rather than direct evidence of technology implementation effectiveness.
This study examines how data-driven decision-making and information systems shape supply chain efficiency, sustainability practices, and technological integration in an urban agriculture enterprise, with a focus on rejection and redistribution procedures as a decision-support mechanism for waste management. The objectives were to assess how real-time monitoring tools, IT-based order-processing platforms, and other information systems support supply chain and sustainability decision-making, and how such systems inform the firm's waste-management strategy. A quantitative approach was adopted, stratifying 100 stakeholders consumers, suppliers, farm operators, and distribution partners using structured questionnaires, with descriptive statistics applied to assess performance across key decision areas. A purposively selected agribusiness enterprise committed to supply chain performance was found to have moderate-to-high efficacy in timely delivery management, real-time monitoring tools, and order-processing IT platforms that collectively support operational decision-making. Sustainability is improved through decisions favouring biodegradable packaging and local, seasonal sourcing. Technological integration, including transparency mechanisms and smart packaging, informs the firm's decision processes, although blockchain adoption remains limited in practice. Effective rejection and redistribution decision processes reduce waste, divert surplus to productive channels, and directly support the company's zero-waste objectives. The findings show that supply chain excellence and environmental responsibility are mutually reinforcing when decision-making is supported by the right information systems: biodegradable packaging, in-house waste-management systems, and selective use of emerging technologies collectively strengthen operational performance. By embedding data-driven decision-support at every stage of its supply chain, the enterprise offers a model for responsible, information-systems-enabled agritech decision-making that balances efficiency, food safety, and long-term environmental sustainability.
Open access
Food Waste Reduction and Sustainability
Agriculture Sustainability and Environmental Impact
Abstract Artificial Intelligence (AI) has revolutionized supply chain management by improving decision-making, sustainability, and operational efficiency. Startups in sustainable agriculture are depending more and more on AI-powered technology to boost traceability throughout the agricultural value chain, optimize output, cut waste, and enhance logistics. Businesses have been prompted to include intelligent supply chain systems that reduce environmental impacts while guaranteeing product quality and transparency due to the increased consumer demand for environmentally friendly products. By analyzing recent research, identifying AI applications, talking about implementation issues, and putting forth a conceptual framework for sustainable AI-driven supply chains, this paper investigates the role of AI in supply chain management for eco-friendly products and sustainable agriculture startups. Using a methodical approach to literature research, the study synthesizes information from international organizations, industry publications, and peer-reviewed journals. Demand forecasting, precision agriculture, inventory optimization, cold-chain monitoring, transportation efficiency, blockchain-enabled traceability, and circular economy practices are all greatly improved by AI, according to the results. But obstacles including high implementation costs, inadequate digital infrastructure, cybersecurity issues, and a lack of skilled workers continue to pose serious problems for companies. In order to promote social responsibility, economic viability, and environmental sustainability, the paper suggests an integrated AI-enabled sustainable supply chain framework. Keywords: Artificial Intelligence, Sustainable Agriculture, Supply Chain Management, Eco-Friendly Products, Agriculture Startups, Green Supply Chain, Machine Learning, Blockchain.
Food supply chains continue to be susceptible to fraud, contamination incidents, and unclear provenance records, which erode consumer confidence and significantly harm the world economy each year. Because blockchain technology provides immutable, shareable, cryptographically verified ledgers among people who distrust each other, it is frequently suggested as a solution. The oracle problem, however, is inherited by the majority of deployed systems: a ledger ensures that recorded data is not altered, but it does not ensure that the data was accurate when it was entered. In addition to reviewing the opportunities it presents for food safety and sustainability reporting, this study examines the technological, financial, and regulatory obstacles of blockchain-based food traceability and proposes a new architecture called the Dynamic Trust-Weighted Oracle Consensus (DTW-OC) framework. We present the architecture, the scoring algorithm, a comparison against Proof-of-Work, Proof-of-Stake, and PBFT, an example dairy cold-chain scenario, and a research agenda for standardisation and interoperability. DTW-OC introduces a reputation-weighted, cross-validated oracle layer that scores every IoT sensor and human data source in real time and feeds that score into block-validator selection, so a source's influence on the ledger is proportionate to its demonstrated reliability.
Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems.
Blockchain technology offers a distributed, immutable ledger that can improve transparency, traceability and trust among multiple parties in logistics and supply-chain networks. This expanded review synthesises systematic literature from 2018–2025, presents key real-world case studies, quantifies reported benefits, catalogues persistent challenges, and examines the convergence of blockchain with IoT sensors, artificial intelligence and digital twins. Emphasis is placed on food traceability, maritime shipping, pharmaceuticals, sustainability reporting and the socio-technical conditions required for successful adoption. The review also draws on related recent work on supply-chain resilience, digital twins, AI–blockchain integration and emerging quantum approaches to logistics optimization. Illustrative figures and a market-growth curve accompany the analysis. Keywords: Blockchain; Logistics; Supply Chain Management; Traceability; Smart Contracts; Trade Lens; IBM Food Trust; Permissioned Ledger; Interoperability; Digital Twin; IoT Integration; Literature Review; Supply Chain Resilience; Quantum Logistics.
The digital transformation of agricultural supply chains requires efficient coordination among heterogeneous stakeholders and reliable information exchange across distributed logistics networks. As a key component linking agricultural production and downstream distribution, collaboration between agricultural product distribution and textile packaging enterprises has become increasingly dependent on intelligent communication and data-sharing infrastructures. This study systematically investigates the strategic management mechanisms and implementation pathways for collaborative development by integrating transaction cost economics, complex adaptive systems theory, and network effects theory. A four-dimensional management framework encompassing technological support, organizational coordination, benefit distribution, and risk prevention is established, in which entropy weight–TOPSIS is employed for strategic objective alignment, blockchain-based architectures enable trusted information sharing, Shapley value optimization supports dynamic benefit allocation, and Value-at-Risk (VaR) models facilitate quantitative risk control. The proposed framework further incorporates smart contracts and permission-controlled data interaction to improve collaboration efficiency while preserving data security. The resulting management architecture provides a quantitative and scalable solution for digital supply chain coordination and demonstrates practical value for intelligent logistics systems. Moreover, its distributed information-sharing mechanisms and network-oriented optimization strategies offer methodological references for communication-enabled industrial ecosystems, wireless sensing infrastructures, and electromagnetic information transmission environments requiring reliable multi-node coordination and secure data exchange.
This chapter presents the concept of a Smart Rice Mill as an intelligent, connected, automated, and traceable rice-processing ecosystem. It integrates IoT sensors, computer vision, deep learning, Edge AI, cloud analytics, predictive maintenance, intelligent control, and blockchain to improve rice-processing operations. The chapter discusses automated grain inspection, variety classification, defect detection, broken-rice estimation, milling-quality prediction, machine monitoring, process optimization, and digital recording of batch history. It also examines implementation challenges involving legacy machinery, hardware and sensor reliability, cybersecurity, staff training, integration, and economic feasibility. The proposed future direction is a closed-loop Smart Rice Mill capable of sensing paddy and machine conditions, predicting quality, adjusting processing parameters, verifying output, and maintaining complete traceability.
This chapter proposes an integrated Blockchain–IoT–AI framework for secure and intelligent quality traceability, particularly in agricultural and rice supply chains. It explains how IoT sensors can continuously collect physical and environmental information, AI models can analyze images and sensor data for quality assessment, and blockchain can securely record important quality events and processing information. The framework supports unique digital identities for rice batches, quality monitoring, defect detection, moisture estimation, quality scoring, and QR-based access to traceability information. The chapter examines applications in rice quality certification, smart rice mills, food safety, warehouses, export-quality monitoring, consumer verification, and government procurement. Challenges related to data quality, sensor reliability, interoperability, stakeholder participation, scalability, and regulatory coordination are also addressed.
The reliable delivery of temperature-sensitive pharmaceuticals depends on an unbroken cold chain governed by Good Distribution Practice (GDP). As biologics, vaccines, plasma-derived products, and advanced therapy medicinal products expand their share of the global medicines market, the clinical and economic consequences of thermal excursions have intensified. This paper reviews recent advances in cold chain integrity and GDP across the regulatory and scientific foundations of temperature control, the engineering of thermal protection and monitoring, the digital transformation of distribution networks, and the systemic dimensions of equipment reliability, sustainability, economics, and equitable access. It examines how passive and active thermal protection systems have improved through vacuum insulation and engineered phase change materials, how real-time monitoring built on connected sensing has displaced retrospective data capture, and how predictive analytics, distributed ledgers, and digital twins are reshaping visibility and traceability. Focused attention is given to the ultra-cold and cryogenic chains that support messenger ribonucleic acid vaccines and cell and gene therapies, where chain of identity and chain of custody requirements compound the demands of thermal control. The paper also considers quality risk management and validation, the reliability of refrigeration assets and the role of predictive maintenance, sustainability pressures such as refrigerant phase-down and single-use packaging waste, the economics of failure and of investment in monitoring, the persistent last-mile gaps in low- and middle-income settings, and the lessons drawn from the pandemic deployment of temperature-sensitive vaccines. The central finding is that cold chain assurance is shifting from a document-centric, compliance-driven discipline toward a data-driven, predictive, and risk-based model. Integrating continuous monitoring with analytics and product specific stability budgets offers the clearest path to reducing wastage while preserving patient safety, although interoperability, validation, cybersecurity, and equitable access remain unresolved challenges.
Khoya (khoa or mawa) is a traditional dairy product, prepared by heating and concentrating milk, which is widely used in preparation of indigenous milk sweets. But, challenges such as process variability, quality deterioration, microbial contamination, adulteration, limited shelf life and inefficient supply chain management hinder its production and distribution. New solutions to these challenges are available across the khoya value chain due to recent advancements in artificial intelligence (AI) and Industry 4.0 technologies. This review highlights the applications of AI in khoya processing, packaging, transportation, distribution and quality management. The role of machine learning, deep learning, computer vision, Internet of Things (IoT), digital twins, smart sensors, and blockchain in process optimization, automated quality inspection, adulteration detection, shelf-life prediction, intelligent packaging, cold-chain monitoring, logistics optimization and demand forecasting is explored. We also review AI-enabled analytical tools for rapid and non-destructive quality assessment, such as hyperspectral imaging, electronic nose, and electronic tongue. The review also discusses the contribution of AI to improving food safety, traceability, sustainability and operational efficiency, as well as to reducing post-harvest losses and environmental impacts. Finally, the paper discusses the existing challenges, future research directions, and prospects of AI-enabled smart dairy manufacturing. The review finds that AI can play a significant role in improving the quality, safety, efficiency, and sustainability of the khoya industry and helping its transition to intelligent and data-driven dairy processing.
Meeting the global demand for fresh, minimally processed food requires us to rethink how we monitor food safety. Traditional laboratory methods are often too slow, labor-intensive, and impractical for real-time applications. To overcome these delays, biosensors have emerged as a rapid, highly sensitive, and cost-effective alternative. This study explores how biosensing technology accurately detects pathogens, chemical contaminants like heavy metals and pesticides, and spoilage indicators across dairy, meat, produce, and packaged foods. What makes these tools truly transformative is their seamless integration with modern digital infrastructure. By combining biosensors with the Internet of Things (IoT), artificial intelligence (AI), nanotechnology, edge computing, and blockchain, we can create intelligent, continuous monitoring systems. These interconnected frameworks allow for real-time, farm-to-fork traceability, enabling early hazard detection, extending shelf life, and significantly reducing food waste through data-driven decisions. Despite this immense potential, bringing smart biosensors to the commercial market involves overcoming distinct practical hurdles. We examine current technical barriers, including biofouling, long-term sensor stability, power management, and high manufacturing costs. More importantly, we highlight the emerging innovations actively solving these bottlenecks, such as biodegradable materials, battery-free platforms, advanced printed electronics, and smart packaging technologies.
Akhter Javed, Huma Gul, Ali Husnain, Rahmat Said · 5 authors
Background: In this study, the increased complexity of today supply chains and explain why conventional forecasting and inventory management techniques are inadequate in today's dynamic and uncertain market conditions. As globalization and data increase, AI has become a gamechanger in delivering better demand forecasting and inventory management, in turn driving a better operation and cost savings. Objectives: This study seeks to assess the performance of AI-based demand forecasting models combined with inventory optimization methods on improving the overall performance of the supply chain. Methods: A quantitative, data-driven methodology was employed, and secondary data were used, including historical demand, inventory levels, and other external data that included seasonality and economic indicators. Demand forecasting models: Advanced machine learning and deep learning models such as Long Short-Term Memory (LSTM), Random Forest and Gradient Boosting were used for demand forecasting. The results of the forecasts were fed into an inventory optimization system using reinforcement learning for dynamic decision-making. Standard deviations like Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to measure the model's performance along with cost-performance analysis. Results: The accuracy of the prediction is significantly higher in AI-based models, especially the LSTM model, than the traditional models, which decreases the errors of the prediction and enhances its responsiveness. AI-powered inventory optimization resulted in significant savings on inventory holding and shortage/cost of order, and improved service levels and inventory stockout rates. The use of external data had yet further improved predictive performance. Conclusion: AI-powered demand forecasting and inventory optimization offer a solid solution to improve the efficiency of the supply chain, make intelligent decisions and minimize operational costs. References Ahn, H. I., Song, Y. C., Olivar, S., Mehta, H., & Tewari, N. (2024). GNN-based probabilistic supply and inventory predictions in supply chain networks. arXiv. Albayrak Ünal, Ö., Erkayman, B., & Usanmaz, B. (2023). Applications of artificial intelligence in inventory management: A systematic review of the literature. Archives of Computational Methods in Engineering. Advance online publication. https://doi.org/10.1007/s11831-023-09977-2 Ayub, M. I., Gharami, A. K., Nitu, F. N., Uddin, M. N., Islam, M. I., Nijhum, A. M., … Yezdani, S. (2025). AI-driven demand forecasting for multi-echelon supply chains: Enhancing forecasting accuracy and operational efficiency through machine learning and deep learning techniques. Emerging Frontiers Library for The American Journal of Management and Economics Innovations, 7(7), 74–85. Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: A multiple case study research. International Journal of Production Research. Advance online publication. https://doi.org/10.1080/00207543.2024.2330633 Choi, T. M. (2022). Supply chain analytics and AI-driven forecasting. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04652-6 Dolgui, A., Ivanov, D., & Sokolov, B. (2022). Reconfigurable supply chain systems. International Journal of Production Research, 60(2), 413–440. https://doi.org/10.1080/00207543.2021.1897179 Douaioui, K., Oucheikh, R., Benmoussa, O., & Mabrouki, C. (2024). Machine learning and deep learning models for demand forecasting in supply chain management: A critical review. Applied System Innovation, 7(2), 40. https://doi.org/10.3390/asi7020040 Fatima, A., & Salam, M. A. (2026). A data-driven predictive framework for inventory optimization using context-augmented machine learning models. arXiv. Ghodake, S. P., Malkar, V. R., Santosh, K., Jabasheela, L., Abdufattokhov, S., & Gopi, A. (2024). Enhancing supply chain management efficiency: A data-driven approach using predictive analytics and machine learning algorithms. International Journal of Advanced Computer Science and Applications, 15(4). Islam, M. K., Ahmed, H., Al Bashar, M., & Taher, M. A. (2024). Role of artificial intelligence and machine learning in optimizing inventory management across global industrial manufacturing and supply chain: A multi-country review. International Journal of Management Information Systems and Data Science, 1(2), 1–14. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. International Journal of Production Research, 59(18), 5633–5645. https://doi.org/10.1080/00207543.2020.1768450 Jin, Z. L., Maasoumy, M., Liu, Y., Zheng, Z., & Ren, Z. (2025). Stochastic optimization of inventory at large-scale supply chains. arXiv. Judijanto, L., Riandari, F., & Marsoit, P. T. (2024). Leveraging AI for optimization in supply chain decision support. Jurnal Teknik Informatika. Kache, F., & Seuring, S. (2022). Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management, 42(1), 1–30. https://doi.org/10.1108/IJOPM-02-2021-0129 Kagalwala, H., Radhakrishnan, G. V., Mohammed, I. A., Kothinti, R. R., & Kulkarni, N. (2025). Predictive analytics in supply chain management: The role of AI and machine learning in demand forecasting. Advances in Consumer Research, 2, 142–149. Kamble, S. S., Gunasekaran, A., & Sharma, R. (2023). Modeling blockchain-enabled traceability in supply chains. International Journal of Information Management, 68, 102509. https://doi.org/10.1016/j.ijinfomgt.2022.102509 Kaul, D., & Khurana, R. (2022). AI-driven optimization models for e-commerce supply chain operations: Demand prediction, inventory management, and delivery time reduction with cost efficiency considerations. International Journal of Social Analytics, 7(12), 59–77. https://doi.org/10.4018/IJSA.315876 Liu, R., & Vakharia, V. (2024). Optimizing supply chain management using hybrid AI models. Journal of Organizational and End User Computing, 36(2), 1–18. https://doi.org/10.4018/JOEUC.347356 Min, H. (2022). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 25(3), 289–303. https://doi.org/10.1080/13675567.2020.1849508 Mitta, N. R. (2023). AI-driven optimization of supply chain networks in manufacturing: Utilizing machine learning for demand forecasting, inventory management, and logistics efficiency. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 404–446. Nweje, U., & Taiwo, M. (2025). Leveraging artificial intelligence for predictive supply chain management: Focus on how AI-driven tools are revolutionizing demand forecasting and inventory optimization. International Journal of Science and Research Archive, 14(1), 230–250. Pasupuleti, V., Thuraka, B., Kodete, C. S., & Malisetty, S. (2024). Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management. Logistics, 8(3), 73. https://doi.org/10.3390/logistics8030073 Patil, D. (2024). Artificial intelligence-driven supply chain optimization: Enhancing demand forecasting and cost reduction (SSRN Working Paper No. 5057408). SSRN. https://doi.org/10.2139/ssrn.5057408 Queiroz, M. M., & Telles, R. (2023). Big data analytics in supply chain management: A review. Transportation Research Part E: Logistics and Transportation Review, 170, 102987. https://doi.org/10.1016/j.tre.2022.102987 Sajja, G. S., Addula, S. R., Meesala, M. K., & Ravipati, P. (2025). Optimizing inventory management through AI-driven demand forecasting for improved supply chain responsiveness and accuracy. In AIP Conference Proceedings (Vol. 3306, No. 1, Article 050003). AIP Publishing. Shahnawaz, M., & Safder, A. (2025). Stochastic learning-optimization model for resilient supply chains. arXiv. Shen, L., & Zang, Z. (2024). Enterprise supply chain network optimization algorithm based on blockchain-distributed technology. Information Discovery and Delivery. Advance online publication. Sodhi, M. S., & Tang, C. S. (2021). Supply chain management for extreme conditions. MIT Sloan Management Review, 62(2), 1–8. Tang, W. (2024). Improvement of inventory management and demand forecasting by big data analytics in supply chain. Applied Mathematics and Nonlinear Sciences, 9(1). Verma, P. (2024). Transforming supply chains through AI: Demand forecasting, inventory management, and dynamic optimization. Integrated Journal of Science and Technology, 1(3). Waller, M. A., & Fawcett, S. E. (2021). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010
Shahreza Shauqi Ismail, Nura Abubakar Allumi, Yousif Munadhil Ibrahim
Purpose: The aim of this study is to analyze the evolution of research trends in green supply chain management (GSCM) in agriculture, constructing an intellectual framework to improve the efficiency and environmental sustainability of the supply chain with smart agricultural technologies. Design/methodology/approach: The review used the bibliometric approach with two science mapping approaches (i.e., co-citation and co-word analysis) were perfomed to analyze 381 articles published in the Web of Science (WoS) database to investigate past and future research direction in GSCM using the VOSviewer software. Findings: Previous studies mainly focused on CE integration, sustainable agri-food supply chain design, and blockchain in supply chain management, whereas future research is expected to emphasize green logistics, low-carbon supply chains, smart circular systems, and digital innovation in green agri-SCM. Limitations and Research implications: This study is limited to the WoS database and two bibliometric techniques. Future research should validate the identified themes through additional bibliometric and empirical studies. Practical Implications: This study provides practical insights for managers, policymakers, and researchers to support the development of sustainable agricultural supply chains through circular economy, green logistics, and digital technologies. Originality/value: This study provides a comprehensive knowledge map of GSCM in agriculture by identifying past research themes and future research directions through bibliometric analysis
This article presents the DigInTraCE Blockchain Module, a secure and scalable framework for managing Digital Product Passports (DPPs) and traceability data across industrial supply chains. Built on Hyperledger Fabric, the solution combines distributed ledger technology, cloud-native infrastructure, smart contracts, and standardized EPCIS 2.0 traceability to enable trusted collaboration among multiple stakeholders. The technical article describes the platform architecture, governance mechanisms, secure API integration, identity management, and blockchain-based validation processes that support transparent, interoperable, and auditable product lifecycle information. The proposed framework provides a robust foundation for future Digital Product Passport implementations and circular industrial value chains.
Industrial supply chains involve multiple stakeholders, complex logistics operations, and financial transactions that require transparency, traceability, and secure coordination.Traditional supply chain systems suffer from limited transparency, the risk of data manipulation, and insufficient trust among participants.To address these challenges, this paper proposes a decentralized industrial supply chain management system implemented on an Ethereum-compatible blockchain network.The proposed architecture integrates smart contracts to automate workflows, including stakeholder registration and verification, multi-item order processing, shipment tracking, simulated delivery verification (SDV), and escrow-based conditional payment settlement.The system adopts a hybrid on-chain/off-chain storage architecture in which transactional records are maintained on-chain, while raw material and product images are stored off-chain using the InterPlanetary File System (IPFS).This design reduces blockchain storage overhead while preserving data integrity through cryptographic hash references.To improve operational efficiency and reduce overhead from repeated transactions, the proposed system supports multi-item batch transactions during procurement and ordering, while the logistics and settlement stages maintain per-item execution to preserve traceability and accountability.Experimental evaluation was conducted on the Celo Sepolia network to measure gas consumption and transaction fees for both batch-based and functionally equivalent per-item execution workflows under controlled conditions.The evaluation included multiple predefined workload configurations, and statistical analysis using mean and standard deviation was performed to assess execution stability.The results indicate that transaction aggregation reduces gas consumption by approximately 40-43% for raw material order creation and by 40-48% for raw material operations (addToMultipleCart).Product aggregation workflows also demonstrated measurable gas-efficiency improvements.These findings demonstrate the efficiency benefits of multi-item transaction aggregation within the proposed implementation while preserving lifecycle traceability and escrow-enabled settlement correctness.The reported results represent controlled implementation-level efficiency measurements within the proposed blockchain-based supply chain architecture.
Md. Safaet Hossain, Mohammad Shakibul Hasan Sakib, Md. Rayhan Ahmed Shis, Sakib Ahmed · 5 authors
Modern food supply chains, particularly those involving essential commodities like rice, often suffer from major challenges such as product fraud, inefficient record-keeping, and a lack of consumer trust. Traditional centralized systems are prone to data tampering, limited transparency, and poor traceability, making it difficult to verify the authenticity and origin of goods. To address these issues, our research introduces TraceRoot, a blockchain-based traceability framework designed to enhance transparency, accountability, and trust in agricultural supply chains.TraceRoot leverages the immutability and decentralization of blockchain technology to maintain a secure, distributed ledger that records every transaction and movement of goods across the supply chain. Each stakeholder including farmers, distributors, retailers, and consumers has role-based access to authenticated data through a user-friendly interface. The framework integrates smart contracts to automate transactions and digital signatures to verify the integrity of the data being uploaded, minimizing the risk of human error or manipulation
The global dairy industry confronts a persistent structural challenge in operationalising food safety and animal welfare compliance. Manual inspection regimes and intermittent audits are demonstrably inadequate for the heterogeneous, geographically dispersed landscape of small-scale farming, where data integrity, real-time monitoring capability, and regulatory transparency are simultaneously compromised. This article presents GreenDairyChain, an integrated compliance innovation framework that synthesises four enabling technologies: GreenEdgeML (a lightweight TinyML inference engine optimised for microcontroller-class devices), Privacy-Preserving Federated Learning (FL) with Graph Attention Network (GAT)-based dynamic clustering, Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) for cryptographic compliance verification, and a Layer-2 Polygon zkEVM Blockchain with domain-specific smart contracts governing farm identity, violation detection, audit triggers, and licence management. GreenEdgeML executes multimodal sensor fusion across four signal modalities (body temperature, accelerometer activity, ammonia concentration, and milk pH) entirely on-device using 8-bit integer quantisation, consuming 64.6 KB RAM and 82.7 mW per inference cycle on the ESP32 platform. The FL engine employs GAT-based farm clustering with DBSCAN outlier exclusion to address non-IID data heterogeneity while maintaining Byzantine fault resilience. Compliance inferences are encoded as R1CS arithmetic circuits (14,240 constraints) and verified on-chain at O(1) cost through ZK-SNARK proofs generated in 1.25 seconds. Evaluated on the Shahhet28121 benchmark dataset across 16 biomarkers, the full system achieves 96.94% global classification accuracy, a 97.7% reduction in per-round communication payload (4.25 KB), and maintains classification accuracy above 90% under 20% Gaussian sensor noise. Ablation experiments confirm that each architectural component contributes independently to system performance. The findings carry implications for green business innovation, sustainable agriculture governance, and the design of trustworthy AI ecosystems in resource-constrained rural contexts.
Healthcare supply chains face increasing challenges related to counterfeit products, fragmented information flows, limited traceability, and insufficient coordination among distributed stakeholders.Existing centralized and partially decentralized approaches still encounter difficulties in maintaining immutable records, real-time verification, and trusted operational transparency across the pharmaceutical distribution process.This study investigates a distributed medical supply chain framework that improves traceability, compliance control, and operational reliability in healthcare logistics.A blockchain-enabled architecture was developed by integrating dynamic quick response (QR)-based identification, customizable smart contracts, and a hybrid consensus mechanism combining Proof-of-Work (PoW) and Proof-of-Stake (PoS).The framework assigned a unique cryptographic identity to each medicine unit and supported end-to-end verification through blockchain-linked QR validation.Smart contracts were designed to automate ownership transfer, compliance checking, and counterfeit detection throughout the supply chain workflow.The framework was implemented and evaluated in a simulated distributed environment using pharmaceutical transaction scenarios.The experimental results showed that the proposed approach achieved average validation accuracy of approximately 98.1%, maintained transaction throughput between 150 and 320 transactions per second (TPS), and reduced consensus delay through adaptive PoW-PoS coordination.The system also demonstrated strong resistance to forgery attempts and stable operational performance across repeated validation experiments.The results indicate that integrating blockchain governance mechanisms with QR-enabled authentication can improve transparency, trust, and traceability in distributed healthcare supply chains.The proposed framework provides a scalable systems engineering solution for pharmaceutical logistics management and offers a practical foundation for compliance-oriented digital transformation in healthcare supply networks.
Abstract Food security and the stakeholders’ trust are essential to ensure that agricultural supply chains are transparent and secure. This research presents a Queueing-Assisted Blockchain Smart Contract (QABSC) framework to enhance end-to-end traceability in the millet supply chain. The framework incorporates fog computing into real-time data processing to reduce latency and optimizes transaction flow using queueing techniques, thereby ensuring an efficient and scalable blockchain supply chain platform. The Internet of Vehicles and Things (IoVT) connects cars, sensors, roadside infrastructure, and cloud and edge technologies to make transportation and mobility smarter. By integrating fog-layer intelligence with blockchain-based immutable record-keeping, Internet of Vehicles and Things enabled sensing and vehicular logistics, and end-to-end visibility, the proposed system may ensure tamper-resistant monitoring of millet products from farms to customers. Internet of Things (IoT) sensors collect real-time information about millet quality and storage conditions. This data is securely stored using the InterPlanetary File System (IPFS) and verified by smart contracts on a distributed ledger. This approach ensures automated compliance verification for auditors and regulators, immutable data storage, and conditional privacy. The proposed model reduces bottlenecks in blockchain transaction processing and enhances efficiency, privacy, data integrity, and trust among producers, distributors, retailers, farmers, and buyers. The proposed model is evaluated based on key performance metrics. The experimental evaluations of the proposed framework demonstrate enhanced throughput, improved transparency, reduced computational overhead, and robust security. This research focuses on a unique integration of smart contracts, queueing theory, IPFS, Fog Computing, IoT devices, and blockchain technology to promote sustainable and transparent millet supply chain management.
Rouwaida Abdallah, Guillermo Toyos Marfurt, Sara Tucci-Piergiovanni
This work has been accepted for publication in the proceedings of 3SCEA 2026 conference. The deposited manuscript corresponds to the author-accepted version presented at the conference. The final published version will appear in the official conference proceedings. Abstract: Traceability remains a critical challenge in modern supply chains, particularly as industries transition towards sustainability and circular economy models. The Digital Product Passport (DPP) emerges as a vital tool to consolidate and share comprehensive product information across its lifecycle. In this paper, we propose a decentralized, customizable, and self-deployable DPP system, leveraging blockchain technology and an extension of the Fractional Non-Fungible Token (F-NFT) model. This approach enables fine-grained traceability of individual product components and events across the supply chain, ensuring transparency and verifiability. A key strength of our system lies in its flexibility, enabling businesses to deploy tailored solutions without reliance on centralized service providers. The proposed system empowers stakeholders with greater control over product data while supporting selective information sharing. We present a functional implementation of the system and discuss the crucial design decisions that support its real-world applicability.
The integration of smart contracts is transforming logistics and supply chain management (LSCM) by improving transparency, visibility, and accountability. This study examines how automated digital agreements and secure nutritional labeling enhance credibility and safety in the food industry. Using encrypted ledgers and records, smart contracts help address challenges such as counterfeit products, fraudulent labeling, and ethical violations. The study aimed to evaluate smart contracts as a strategic tool for managing information throughout a food product’s lifecycle, with emphasis on sustainability and ethical LSCM practices. A quantitative methodology was used, collecting survey data from 130 urban consumers in India who shop both online and offline. The survey captured consumer views on ethical consumption, organic versus processed foods, eco-friendly packaging, and pricing transparency. Findings show that although smart contracts are still emerging in the food sector, they can address major systemic issues. By defining accountability measures, these contracts can align societal well-being with industrial efficiency. This paper contributes to supply chain management research by highlighting the shift toward distributed information systems and emphasizing the importance of smart contracts in creating a transparent, ethical, and safe food supply chain for modern consumers.