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.
The fast-developing Industry 4.0 has brought about shifts into our food and nutrition systems with automation, data analytics and digital connectivity. This systematic review investigates how IoT, AI, robotics and blockchain interact with each other on the relation between food systems and safety, traceability and personalized nutrition. In accordance with the PRISMA framework, 68 studies (2013–2025) were synthesized across food production, processing and nutrition settings. Results indicate that Industry 4.0 technologies increase transparency, save 20-40% wastage, and make precision nutrition possible through predictive analytics but some obstacles exist such as cost, infrastructure and regulation. This review presents a cross-sectorial view of digital transformation in food and nutrition systems and identifies research priorities in the path towards human-AI collaboration, sustainable innovation in Industry 5.0.
Food Supply Chain Traceability
Smart Agriculture and AI
Agriculture Sustainability and Environmental Impact
The security, integrity, and trustworthiness of heterogeneous data have become a burning issue in the rapidly changing smart agriculture environment. This chapter discusses how machine learning (ML) and blockchain technologies can be integrated to ensure the security of agriculture-based applications in the Internet of Multimedia Things (IoMT). It explains how multimodal agricultural data, including sensor measurements, satellite pictures, videos taken by drones, and farmer feedback, can be smartly analyzed with the help of ML and deep learning algorithms and safely stored and shared with the help of blockchain systems. The chapter brings to the fore ML-based methods in detecting anomalies, predicting yields, detecting diseases, and decision support and blockchain capabilities of decentralization, immutability, smart contracts, and traceability. The proposals of the architectural models of ML-blockchain-based agricultural systems are introduced with a focus on secure data exchange, access management, and trust management. Practical use cases such as supply chain monitoring, precision farming, and sustainable resource management are also discussed in the chapter and end with the main challenges, limitations, and future research directions.
ABSTRACT Freezing is essential for maintaining the stability of the global supply chain for protein‐based foods such as meat and aquatic products. However, traditional thawing technologies suffer from low efficiency and severe quality deterioration. Aligned with the principles of Industry 5.0, next‐generation food thawing increasingly prioritizes intelligence, human‐centricity, sustainability, and resilience. This paper systematically examines the entire technological chain from freezing pretreatment to intelligent integration. Rather than providing a technology‐by‐technology summary, this review establishes a progressive analytical framework that connects freezing pretreatment, thawing strategies, intelligent systems, and Industry 5.0 enabling technologies. It first discusses freezing pretreatment strategies that establish a quality foundation for subsequent thawing, then reviews individual thawing technologies and synergistic thawing strategies through critical evaluation of their mechanisms, technological advantages, limitations, evidence quality, and industrial applicability. It further analyzes the architecture of intelligent thawing units, including multidimensional sensing networks, intelligent control, and digital twins, and highlights their role as the digital infrastructure for adaptive and data‐driven thawing systems. Then it further discusses the roles of Industry 5.0 enabling technologies in building human‐centric, sustainable, and resilient thawing systems. These technologies include augmented reality, collaborative robots, low‐code platforms, blockchain, edge–cloud collaboration, and closed‐loop feedback optimization. Finally, core challenges and future directions are outlined. This review proposes a whole‐chain optimization framework from freezing pretreatment to intelligent thawing systems and provides critical insights into the transition toward Industry 5.0‐oriented food processing.
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.
Mingqian Li, Rong Du, Andrew Burton‐Jones, Jianing Xie
Purpose Grounded in signaling theory, this study examines whether traceability information displaces or complements incumbent quality cues and contrasts the relative efficacy of blockchain-enabled traceability technologies with traditional systems. Design/methodology/approach This study analyzes 18 months of product-level sales data from a global e-commerce platform using a staggered difference-in-differences design with robustness checks. We apply latent Dirichlet allocation topic modeling to consumer reviews and use a synthetic difference-in-differences approach to examine shifts in consumer attention after traceability implementation. Findings Traceability information increases product sales, particularly for lower-reputation brands and diminishes the effect of electronic word-of-mouth, suggesting that diagnostic quality signals matter more than social information signals. Although blockchain-enabled traceability should enhance signal credibility, its observed impact falls short of expectations. Research limitations/implications The sample is limited to the automotive engine oil context in China. Future research should examine other categories and national contexts. Practical implications Platform managers and emerging brands can deploy low-cost traceability labels to boost demand. Blockchain solutions may require consumer education to justify higher implementation costs. Social implications Augmenting supply-chain transparency and product traceability curbs counterfeit and substandard goods, improves consumer welfare, and supports regulatory and sustainability objectives. Originality/value This study systematically assesses the substitutive and complementary roles of traceability signals in a multi-cue setting, tempers optimism about blockchain-enabled traceability and extends research on digital supply-chain transparency and signaling theory.
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.
Rafael Hoffmann, Carlos Moratelli, Alex S. R. Pinto
ABSTRACT Background Preserving the quality and safety of perishable products requires continuous monitoring and reliable traceability. Although the Internet of Things (IoT) enables real‐time data collection, multi‐organizational supply chains lack a common mechanism for assigning data custody while maintaining transparency, integrity, and performance. Objective This study proposes and evaluates an architecture integrating IoT, edge/fog computing, and hybrid storage—an off‐chain traditional database combined with a permissioned blockchain—to monitor and trace perishable products. Methods A prototype was implemented using IoT devices and simulators, edge and fog components, and hybrid storage. High‐volume sensor data and critical records were stored off‐chain in MongoDB, while their corresponding hashes were stored on‐chain using Hyperledger Fabric. Four controlled experiments assessed insertion response time, the impact of increasing sensors and edge devices, blockchain queue performance under burst workloads, and blockchain storage consumption. The hybrid approach was compared with MongoDB‐only and Hyperledger Fabric‐only storage. Results Hybrid storage achieved insertion up to six times faster than blockchain‐only storage. Response times increased with simultaneous requests and additional edge devices, while asynchronous ordered insertion prevented transaction conflicts during bursts. The prototype achieved 18.5 transactions per second, below the 65 estimated for an illustrative supply‐chain scenario. Blockchain storage grew approximately 8 MB per 100 records, reaching about 1 GB for 12,800 hashes. Conclusion The prototype demonstrates the feasibility of combining off‐chain storage, permissioned blockchain records, and edge/fog processing to provide verifiable traceability while reducing on‐chain load. Larger‐scale, real‐world evaluations and storage‐management strategies remain necessary.
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. 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Abdulaziz Al-Saab, Amal A.M. Elgharbawy Elgharbawy
This study presents a bibliometric and critical review of global research on halal food control systems, with particular attention to countries developing new halal regulatory frameworks, including Saudi Arabia and the wider Arab region. A systematic review of Scopus-indexed publications from 2010 to 2025 was conducted following PRISMA guidelines. The final global corpus comprised 847 peer-reviewed articles analysed using VOSviewer and Biblioshiny through co-authorship, co-citation, bibliographic coupling, keyword co-occurrence, and thematic evolution analyses. An additional subset of 82 Arab-region studies, covering Gulf Cooperation Council and wider Arab League states, was manually coded using the FAO/WHO five-component national food control system framework. The field grew at an annual rate of 18.3%, with Malaysia and Indonesia contributing 43% and 28% of publications, respectively. Five major research clusters were identified: fatwa-based legislation; multi-agency governance; inspection, enforcement, and laboratory systems; information, education, communication, and training; and emerging technologies, including blockchain and artificial intelligence. Despite rapid growth, the literature remains geographically concentrated, theoretically underdeveloped, methodologically homogeneous, and largely silent on the cost-benefit implications of halal control systems. Few studies integrate Maqasid al-Shari’ah with risk-management approaches or examine halal governance as a complete regulatory system. By applying the FAO/WHO framework, this review moves beyond isolated certification and supply-chain perspectives. It demonstrates that Arab-region halal governance exhibits a distinctive “law-rich but evidence-poor” profile and proposes a research agenda addressing institutional performance, empirical evidence, regulatory effectiveness, and economic trade-offs.
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.