Financial institutions increasingly rely on sophisticated database architectures to gain competitive advantages in high-frequency trading and analytics environments. This article examines optimal database technologies for financial applications, comparing in-memory, columnar, time-series, and distributed ledger architectures across standardized financial workloads. Multiple case studies demonstrate how different architectures excel in specific contexts: in-memory processing delivers superior performance for order processing, columnar storage enables faster analytical queries for market analysis, while time-series databases efficiently handle pattern recognition for fraud detection. Performance bottlenecks, consistency trade-offs, regulatory compliance challenges, and security considerations are explored in depth. The results indicate that no single architecture provides optimal performance across all financial application requirements; instead, financial institutions must select technologies based on specific use cases, with heterogeneous architectures often delivering superior results. The article concludes by examining emerging technologies with potential to transform financial database landscapes, including persistent memory, hardware acceleration, specialized indexing structures, AI-integrated engines, and hybrid blockchain solutions.
The pharma supply chain has various issues, including counterfeiting, visibility, and product authenticity in the market. Some of the issues threatening the safety of the patients and the efficacy of the industry include those named above. Such issues are overcome by new technologies like Blockchain and Artificial Intelligence (AI) that are being offered. Blockchain enhances transparency since it is a distributed ledger technology that traces the drug through the supply chain and minimizes the risk of counterfeit drugs. In contrast, there is AI that extends beyond data acquisition and analytics to forecast supply chain disruptions, inventory controls, and even drug shortages to improve decision-making and performance. This paper seeks to focus on the issues that are overcome by Blockchain when integrated with Business Intelligence (BI) systems, such as drug authenticity and counterfeiting issues. This highlights the emphasis that there is a need to consider technology integration when there is a need to make a change, security considerations, and the integration of AI to make the supply chain in the pharma industries more efficient and reliable.
This article explores the transformative integration of generative AI capabilities with Data Mesh architecture to revolutionize enterprise analytics. Beginning with examining traditional data architectures' limitations, the discussion highlights how centralized proceeds towards creating bottlenecks that impede innovation and time-to-insight. The Data Mesh paradigm is presented as a fundamental shift that decentralizes data ownership while maintaining federated governance. The integration of generative AI within this framework enables natural language interfaces, synthetic data generation, automated documentation, and intelligent insight creation. Implementation strategies using Databricks platform capabilities demonstrate how organizations can balance domain autonomy with enterprise interoperability. The architecture delivers enhanced analytics through AutoML-powered data quality with generative explanations and event-driven processing that enables real-time, predictive intelligence. Together, these capabilities create a self-improving ecosystem that democratizes data access while ensuring governance, ultimately enabling organizations to move beyond traditional reporting toward autonomous, data-driven operations with cross-domain collaboration.
This document provides a comprehensive analysis of sustainable data engineering practices, focusing on the ecological implications of contemporary methodologies. It examines power usage, carbon dioxide output, and electronic waste production in data centers, while exploring eco-friendly approaches such as energy-conserving hardware, streamlined data handling processes, and the adoption of sustainable power sources. The potential of AI enhanced optimization techniques, quantum computation, and distributed ledger systems to reduce environmental impact is also examined. The paper concludes with actionable strategies for corporations and regulators to enhance the sustainability of data engineering practices, ensuring that the expansion of our digital landscape does not occur at the cost of environmental health.
Smart cities take advantage of digital services to enhance citizens’ experiences. Integration processes facilitate interactions between these services, providing or improving functionalities. The integration can operate under specific restrictions, which can be represented through smart contracts deployed in a blockchain. Monitoring systems track interactions between integration processes and digital services by recording events from communication ports. In this paper, we argue that current monitoring tools lack the ability to observe these ports or invoke smart contracts. We propose a monitoring system to track integration processes, capture port-reported events, and invoke smart contracts on a blockchain platform.
This research examines the risk profiles of XRPUSD and ADAUSD cryptocurrencies through Value at Risk (VaR) analysis with Monte Carlo simulation, providing quantitative risk assessments for both individual assets and a diversified portfolio. Analyzing historical price data from January 2016 to November 2024, the study identifies distinctive risk characteristics between these cryptocurrencies: ADAUSD exhibited marginally higher historical returns (1.44% monthly) compared to XRPUSD (1.42%), but with notably higher volatility (standard deviation of 5.41% versus 4.65%). The Monte Carlo simulation with 1,000 iterations generated VaR estimates at multiple confidence levels, revealing that XRPUSD consistently demonstrated lower downside risk than ADAUSD across all confidence thresholds. At the 99% confidence level, ADAUSD showed a Mean VaR of -10.97%, indicating potential monthly losses exceeding $10.97 million on a hypothetical $100 million investment, while XRPUSD's lower Mean VaR of -9.52% translated to potential losses of approximately $9.52 million. The most striking finding emerged from the portfolio analysis, which revealed dramatic risk reduction through diversification—the equally-weighted portfolio achieved a Mean VaR of merely -2.22% at the 99% confidence level, representing an approximately 80% reduction in potential losses compared to ADAUSD alone. These results demonstrate that cryptocurrency diversification can substantially mitigate extreme downside risk while maintaining exposure to the digital asset class. The significant risk reduction achieved through a simple two-asset allocation validates the application of modern portfolio theory principles to cryptocurrency investments despite their unique characteristics and underscores the critical importance of diversified approaches rather than concentrated positions for risk-conscious cryptocurrency investors. This research contributes to both theoretical understanding of cryptocurrency risk dynamics and practical portfolio construction approaches, providing quantitative evidence for the value of diversification strategies in navigating the substantial volatility inherent in digital asset markets.
Purpose The integration of blockchain technology and artificial intelligence (AI) is reshaping the financial services industry, offering transformative solutions in areas such as risk management, fraud detection, regulatory compliance and operational efficiency. Design/methodology/approach This paper presents a systematic literature review of over 100 peer-reviewed studies published between 2020 and 2024, analyzing the benefits, challenges and future directions of blockchain-AI applications in financial services. Our findings reveal that while blockchain enhances data integrity, security and transparency, AI drives predictive analytics, automation and decision-making efficiency. Findings The synergy of these technologies holds significant potential yet faces critical challenges related to scalability, interoperability, regulatory compliance and ethical AI governance. We identify key research gaps, including the lack of standardized regulatory frameworks, limited real-world case studies and technical barriers to integration. To address these gaps, we propose a comprehensive theoretical framework linking technological advancements to regulatory and ethical considerations. This study contributes to both academic discourse and industry practice, offering actionable insights for financial institutions, technology developers and policymakers navigating the rapidly evolving FinTech landscape. Research limitations/implications The rapidly evolving nature of blockchain and AI technologies may limit the long-term applicability of some findings. The study primarily focuses on published academic literature, potentially overlooking some industry-specific developments. Future research should address the identified gaps, particularly in cross-chain interoperability, ethical AI frameworks, and long-term economic impacts. Empirical studies and case analyses could further validate the theoretical insights presented in this review. Originality/value This study provides a novel, comprehensive synthesis of blockchain and AI applications in financial services, offering valuable insights for both academics and practitioners. By critically examining the synergies and challenges of these technologies, it presents a unique perspective on their transformative potential in FinTech. The proposed research agenda addresses crucial gaps in current knowledge, guiding future investigations. The findings contribute to a deeper understanding of the complex interplay between technological innovation, regulatory frameworks and ethical considerations in the evolving landscape of financial services.
The evolution of supply chain finance has entered a transformative phase driven by data analytics, artificial intelligence, and digital technologies. This article examines how these innovations are reshaping financial relationships between buyers, suppliers, and financial institutions in supply chains worldwide. It explores the core data technologies revolutionizing supply chain finance, including ERP integration, advanced analytics, and automated financing platforms. The article further investigates how artificial intelligence and machine learning applications enhance credit risk assessment, enable dynamic pricing models, improve fraud detection, and streamline document processing through natural language processing. Additionally, it analyzes the impact of blockchain and distributed ledger technologies in automating payments through smart contracts, expanding access to financing through tokenization, and providing end-to-end traceability. While highlighting the significant benefits of these technologies, the article also addresses implementation challenges related to data quality, system integration, and change management requirements, offering insights for organizations seeking to optimize their supply chain finance operations in an increasingly digital ecosystem.
Purpose This study aims to analyse the academic literature on cryptocurrency regulation using a combined bibliometric and systematic literature review approach, focusing on research trends, influential contributions and thematic clusters from 2018 to 2024. Design/methodology/approach This study reviews 62 journal articles published between 2018 and 2024. A combined bibliometric and systematic literature review approach is used to analyse key articles, journals, authors and countries contributing to the field. Thematic clusters such as crowdfunding, FinTech, blockchain vulnerabilities, Central Bank Digital Currencies (CBDCs) and economic forecasting in developing countries are identified. Findings The analysis reveals emerging trends and significant advancements in cryptocurrency regulation, highlighting key contributors in the field. Thematic clusters show a focus on blockchain vulnerabilities, the rise of CBDCs, and regulatory challenges in developing economies. These themes represent the most pressing areas in cryptocurrency market regulation. Practical implications The findings offer insights for policymakers, researchers and industry practitioners to shape effective regulatory frameworks, addressing critical issues such as blockchain security and central bank digital currencies. Social implications This research contributes to the development of robust regulatory frameworks, promoting market stability and transparency, which will ultimately benefit global financial markets and stakeholders in the cryptocurrency ecosystem. Originality/value To the best of the authors’ knowledge, this study is the first to integrate bibliometric and systematic literature review methods to examine cryptocurrency regulation, providing a comprehensive overview of the research landscape.
Mahmood A. Bazel, Fathey Mohammed, Mazida Ahmad, Abdullah O. Baarimah · 5 authors
Blockchain technology has gained significant attention in several sectors owing to its distributed ledger, decentralized nature, and cryptographic security. Despite its potential to reform the healthcare industry by providing a unified and secure system for health records, blockchain adoption remains limited. This study aimed to identify the factors influencing the intention to adopt blockchain in healthcare by focusing on healthcare providers. A theoretical model is proposed by integrating the Technological-Organizational-Environmental framework, Fit-Viability Model, and institutional theory. A quantitative approach was adopted and data were collected through an online survey of 199 hospitals to evaluate the model. The collected data were analysed using PLS-SEM. The results indicated that technology trust, information transparency, disintermediation, cost-effectiveness, top management support, organizational readiness, partner readiness, technology vendor support, fit, and viability significantly and positively influenced the intention to adopt blockchain-based Health Information Systems in hospitals. Conversely, coercive pressure from the government negatively affects adoption decisions. Moreover, the study found that the hospital ownership type did not moderate the relationship between the identified factors and blockchain adoption. This study provides valuable insights into the various factors that influence blockchain adoption in hospitals. The developed model offers guidelines for hospitals, blockchain providers, governments, and policymakers to devise strategies that promote implementation and encourage widespread adoption of blockchain in healthcare organizations.
Financial services enterprise systems are at a critical inflection point as traditional monolithic architectures struggle to meet evolving market demands, customer expectations, and regulatory requirements. This article explores the transformative potential at the intersection of artificial intelligence, cloud-native microservices, and intelligent data processing for building next-generation financial systems. It examines how these technological paradigms can be leveraged to overcome legacy challenges and regulatory pressures while creating more resilient, compliant, and innovative enterprise architectures. It provides a comprehensive roadmap for transformation, including assessment strategies, incremental modernization patterns, and DevSecOps implementations tailored to financial services. Through case studies of successful implementations and analysis of common challenges, the article offers practical insights for financial institutions navigating this complex evolution. Looking ahead, It identifies quantum-ready architecture, decentralized finance integration, and ambient computing as key developments that will shape future financial enterprise systems, emphasizing the importance of strategic preparation in an increasingly digital financial landscape.
Investment management firms face the critical challenge of maintaining accurate, timely, and consistent data across multiple business functions, with particular emphasis on two fundamental systems: the Investment Book of Record (IBOR) and the Accounting Book of Record (ABOR). While IBOR provides real-time investment intelligence for front-office decision-making, ABOR serves as the official financial record supporting regulatory compliance and reporting. The operational separation between these systems creates significant challenges including data reconciliation complexities, timing mismatches, valuation methodology differences, and corporate action processing issues. Leading firms are pursuing integration strategies such as unified data architecture, near real-time accounting, automated reconciliation tools, API-based integration, and cloud-based solutions. Emerging technologies including artificial intelligence, distributed ledger technology, and outsourcing models are reshaping how investment operations address the IBOR-ABOR divide. These innovations occur against a backdrop of increasing regulatory demands and expanding multi-asset class strategies, driving a transformation in investment data management.
The evolution of regulatory compliance in the financial sector has transformed enterprise data systems from basic record-keeping tools into critical strategic assets. Financial institutions face mounting regulatory requirements across jurisdictions, necessitating sophisticated technological solutions to ensure compliance while maintaining operational efficiency. This article explores how integrated regulatory reporting systems consolidate disparate data sources, real-time monitoring capabilities enable proactive compliance management, and data lake architectures provide comprehensive audit trails. It examines blockchain and distributed ledger technology's role in enhancing transparency and traceability across various compliance domains, including KYC/AML processes, securities settlement, trade finance, and cross-border payments. The article also addresses integration challenges through API-first architectures, data governance frameworks, regulatory change management, and cloud-based platforms. Finally, it explores emerging innovations such as AI-powered regulatory intelligence, predictive analytics, regulatory-as-a-service models, and cross-institutional compliance networks that represent the future of enterprise data systems in regulatory
This study critically reviews the literature on metaverse technologies, developing an integrative framework to explore their sector-specific implications and transformative impact on business management. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. These themes include the metaverse as a tool for economic and environmental policy experiments, navigating financial risk and regulatory dynamics, adapting human resource development to VR-driven environments, Industry 4.0 applications of VR and digital twins, digital twin applications in manufacturing and supply chain optimization, AR and VR in digital marketing and customer experience, AR in enhancing retail and consumer experiences, exploring user interaction and affordances in the metaverse, and VR and AR in tourism experience and engagement. The framework highlights drivers, constraints, and cross-sector linkages, addressing practical challenges such as high implementation costs, regulatory uncertainties, interoperability barriers, cybersecurity risks, and ethical concerns surrounding data privacy and inclusion. The study critically evaluates contradictions in metaverse adoption, such as the tension between sustainability goals and energy-intensive technologies like blockchain, the gap between immersive training potential and workforce adaptation challenges, and the disparity between metaverse-driven economic models and real-world policy implementation hurdles. Research propositions suggest integrating metaverse technologies into business operations while balancing ethical dimensions, psychological impacts, cost limitations, and accessibility barriers. Additionally, the study advocates for expanding theoretical frameworks such as the Resource-Based View (RBV), Technology Acceptance Model (TAM), and experiential learning to account for the dynamic capabilities, risks, and industry-specific constraints of metaverse adoption. Policymakers and practitioners are encouraged to address regulatory and ethical challenges, sectoral disparities, and the unintended consequences of metaverse-driven digital transformation, ensuring operational efficiency, resilience, and consumer engagement while fostering sustainable and inclusive adoption. This research offers actionable insights for strategic implementation, interdisciplinary theoretical expansion, and ethical progress in business management.
Blockchain platform performance is critically important for financial transaction applications. This article presents a case study comparing Ethereum, a public blockchain, with Hyperledger Fabric, a permissioned blockchain, for modeling financial transactions. Key performance metrics evaluated include throughput, latency, transaction cost, and finality. Our findings show that the Ethereum network achieved approximately 15 transactions per second (TPS) with a latency of ~12 seconds and incurred transaction fees of a few U.S. dollars. In contrast, Hyperledger Fabric sustained ~2000 TPS with sub-second latency and negligible cost. Fabric’s deterministic consensus also provides near-instant finality (~1–2 s), contrasting with Ethereum’s probabilistic finality, which requires ~1 minute. The detailed empirical results in Figures and summary Tables comparing core metrics reveal that Hyperledger Fabric offers superior throughput and efficiency for enterprise financial scenarios, while Ethereum’s performance is constrained by its decentralized consensus overhead. All measurements are based on an internal case study deployment without simulation. These insights inform platform selection for financial applications requiring high transaction volume and low latency.
This article explores the integration of artificial intelligence into fintech risk management frameworks, examining how predictive analytics are revolutionizing risk assessment and mitigation capabilities across the financial services industry. It investigates the evolution of risk management within the rapidly changing fintech landscape, highlighting how traditional approaches prove increasingly inadequate in addressing complex challenges like real-time fraud detection, cybersecurity threats, alternative credit assessment, cryptocurrency volatility, and decentralized finance liquidity risks. The article presents a comprehensive analysis of AI-powered solutions across key risk domains, including credit risk assessment, fraud detection, and market risk modeling, demonstrating their superior performance compared to conventional methods. It further outlines a structured framework for enterprise AI implementation, addressing the critical dimensions of data infrastructure, model development, operational integration, and continuous adaptation. The article also examines significant implementation challenges related to regulatory compliance, model explainability, data quality, and talent requirements. Finally, it explores emerging trends that will shape the future of AI-driven risk management, including federated learning, quantum computing, automated risk mitigation, and ecosystem-wide risk intelligence capabilities.
Financial data analytics has become a critical tool for businesses seeking to drive growth, enhance fraud prevention, and mitigate risks in dynamic markets. By leveraging large datasets, advanced algorithms, and real-time analytics, organizations can make more informed financial decisions, improve operational efficiency, and enhance compliance with regulatory frameworks. This review explores how financial data analytics contributes to business growth by improving revenue forecasting, identifying market trends, and optimizing financial planning. Companies can leverage predictive models and artificial intelligence to gain competitive advantages through better risk assessment and investment decision-making. Fraud prevention is another key area where financial data analytics plays a transformative role. Machine learning algorithms, anomaly detection systems, and real-time transaction monitoring help identify and prevent fraudulent activities before they cause significant financial losses. Businesses and financial institutions can use automated risk-scoring models to strengthen security in banking, payments, and investment transactions. Risk mitigation in financial markets is also enhanced through data analytics. By employing predictive modeling, scenario analysis, and stress testing, businesses can assess potential market fluctuations and develop strategies to minimize financial exposure. Moreover, analytics-driven regulatory compliance mechanisms improve transparency and reporting, ensuring adherence to legal and industry standards. Despite its advantages, financial data analytics faces challenges such as data privacy concerns, integration with legacy systems, and the need for skilled professionals. However, emerging technologies, including blockchain, AI, and decentralized finance (DeFi), present new opportunities for strengthening financial security and business resilience. This review concludes that financial data analytics is a vital asset for modern businesses, offering strategic insights that drive profitability, enhance fraud detection, and strengthen risk management. Companies must continue to invest in data-driven solutions to stay competitive in an increasingly digital financial landscape. Keywords: Financial data, Business growth, Fraud prevention, Markets.
Amid growing global pressures to combat climate change, enterprises are reengineering their supply chains to align with carbon-neutral objectives while maintaining operational agility and competitiveness. This study explores the transformative potential of integrating Quantum Artificial Intelligence (QAI), Business Intelligence (BI), and autonomous decision-making technologies in building intelligent, sustainable supply chains capable of minimizing environmental impact. By leveraging the computational advantages of quantum algorithms, machine learning, real-time analytics, and decentralized control systems, organizations can address the increasing complexity of emissions management, logistics optimization, and sustainability forecasting. A comprehensive systematic literature review was conducted following the PRISMA 2020 guidelines, encompassing 97 peer-reviewed articles published between 2010 and 2024 across fields including supply chain management, artificial intelligence, quantum computing, and sustainability analytics. The review reveals that QAI significantly enhances the efficiency of solving combinatorial problems such as routing, scheduling, and emissions prediction, outperforming classical AI in both speed and scalability. BI platforms have evolved from retrospective reporting tools to intelligent systems that facilitate real-time carbon monitoring, dynamic scenario modeling, and sustainability-focused KPI visualization. In parallel, the deployment of autonomous systems—supported by IoT, RFID, edge computing, and AI agents—has enabled decentralized, self-optimizing decision-making across manufacturing, logistics, and procurement functions. Real-world case studies from industry leaders like Siemens, IBM, Honeywell, and John Deere illustrate the tangible impact of these technologies in achieving emissions reductions and improving system-wide sustainability performance. This study provides a comprehensive understanding of how the convergence of QAI, BI, and autonomous systems is shaping the future of carbon-conscious supply chains, offering both theoretical advancement and practical relevance for businesses committed to environmental responsibility and technological innovation.
General Background: Blockchain technology has gained significant global attention due to its potential to enhance transparency, security, and efficiency in various domains, including business forecasting. Specific Background: The integration of blockchain into forecasting mechanisms can improve supply chain efficiency, inventory management, and market demand prediction. Knowledge Gap: Despite its potential, limited research has systematically examined blockchain's role in forecasting capabilities, particularly through a bibliometric analysis approach. Aims: This study employs R Studio and VOSviewer to analyze bibliometric data from Scopus, aiming to identify trends, influential publications, and research gaps in blockchain-based forecasting. Methods: A systematic bibliometric analysis was conducted on 287 relevant articles published between 2015 and 2023, focusing on citation networks, keyword co-occurrence, and thematic clustering. Results: The findings indicate that forecasting is a dominant research theme, with China contributing the most publications. Key studies highlight blockchain's role in cryptocurrency prediction, supply chain management, and decentralized finance. Novelty: This research provides the first comprehensive bibliometric mapping of blockchain-based forecasting, revealing emerging trends and future directions. Implications: The study informs businesses, policymakers, and researchers on leveraging blockchain for predictive analytics, offering insights for enhancing decision-making in finance, trade, and supply chain management.
Implementing multiple reporting sites is another trend that is logical to increase in the financial industry due to the need for instant access to financial information across multiple locations.Yet, there is one crucial issue that needs to be addressed: guaranteeing data consistency across distributed systems is very burdensome because of specific problems related to distributed databases and communication protocols.The topic of this paper is the comparison of the contemporary approaches to storage, with a specific emphasis on the methods that would enhance data integrity and coherency in distributed systems utilized at companies for financial reporting.The discussed techniques include the conventional and modern forms of databases such as relational, NoSQL, Distributed Ledger Technology (DLT), and cloud storage solutions.In this paper, the author reviews the literature and compares and contrasts the benefits and shortcomings of each storage method.Other factors include Transaction Management, Latency, Availability, and Fault tolerances, which are also assessed.In addition, the paper describes the method by which these storage techniques can be deployed in a distributed financial reporting environment.Moreover, the findings of this study point toward the fact that the proposal of the integration of both basic elements of relational databases, as well as of DLT, offers the most durable solution to applied issues of distributed financial reporting.Last, of all, the recommendations of this paper are presented as useful for those financial institutions that use the distributed reporting system and intend to achieve maximum efficiency in further practices, such as choosing the right type of storage depending on certain parameters of operation.
As emerging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI) continue to reshape industries, the need for robust data governance frameworks has become increasingly critical. These technologies introduce unique challenges, including data privacy concerns, security vulnerabilities, and the complexity of managing vast, decentralized data sets. This paper proposes a conceptual framework for data governance tailored to the specific requirements of Blockchain, IoT, and AI technologies. The framework emphasizes a holistic approach, integrating key governance principles such as transparency, accountability, and compliance with regulatory standards. It also highlights the importance of fostering collaboration between stakeholders, including technologists, legal experts, and policymakers, to create a cohesive governance structure that can adapt to the rapid evolution of these technologies. The proposed framework addresses three core areas: data integrity and quality, security and privacy, and ethical considerations. For Blockchain, the focus is on ensuring the immutability and transparency of records while safeguarding against potential misuse of decentralized data. In the context of IoT, the framework prioritizes the management of data from diverse sources, ensuring interoperability and protecting sensitive information from unauthorized access. For AI, the emphasis is on developing ethical guidelines for data usage, preventing bias in algorithmic decision-making, and maintaining transparency in AI-driven processes. The framework also advocates for the integration of advanced data analytics and machine learning techniques to enhance data governance capabilities, enabling real-time monitoring and predictive insights. Additionally, it underscores the need for continuous training and education for all stakeholders to keep pace with the dynamic nature of emerging technologies. By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.
To facilitate flexible manufacturing, modern industries have incorporated numerous modular operations such as multi-robot services which can be expediently arranged or offloaded to other production resources. However, complex manufacturing projects often consist of multiple tasks with fixed sequences, posing a significant challenge for smart factories in efficiently scheduling limited robot resources to complete specific tasks. Additionally, when projects span across factories, ensuring faithful execution of contracts becomes another challenge. In this paper, we propose a modified combinatorial auction method combined with blockchain and edge computing technologies to organize project scheduling. Firstly, we transform efficient resource scheduling into a resource-constrained multi-project scheduling problem (RCPSP). Subsequently, the solution integrates combinatorial auction with random sampling (CA-RS) into smart contracts. Alongside security analysis, simulations are conducted using real data sets. The results indicate that the suggested CA-RS approach significantly enhances efficiency and security in resource arrangement within the industrial Internet of Things compared to baseline algorithms.
The pharmaceutical industry faces critical challenges like counterfeiting and supply chain inefficiencies, jeopardizing public health and the sector’s integrity. This paper introduces the efficient blockchain-enhanced transparent pharmaceutical supply chain management (EBETPSCM) model, which innovatively integrates blockchain and big data analytics to enhance traceability, security, and operational efficiency. At the heart of this model is the strategic use of Hyperledger fabric, renowned for its decentralized consensus mechanism and robust cryptographic methods. This ensures the security and reliability of the supply chain, with its decentralized nature bolstering data immutability, a key factor in maintaining the integrity of supply chain information. Concurrently, big data analytics provide real-time insights, enhancing stakeholder visibility across the chain. Our study critically appraises prevailing challenges, highlighting blockchain’s potential to achieve data immutability and transparency. Empirical evidence from existing studies affirms blockchain’s role in safeguarding pharmaceutical data and refining supply chain operations. The proposed EBETPSCM model integrates a comprehensive framework, addressing technical, methodological, and regulatory aspects. Theoretical outcomes include a well-defined conceptual model, technical insights into blockchain, and big data analytics methodologies. Practically, the study endeavors to implement a prototype system to demonstrate significant improvements in efficiency, transparency, and security. To overcome extant challenges, we advocate for resolving technological issues, enhancing collaborative efforts, and developing new legislative frameworks. The anticipated outcomes promise substantial advancements in safety, efficiency, and transparency within pharmaceutical supply chains. Conclusively, our study emphasizes the necessity of continuous research, collaborative engagement, and regulatory support for the successful adoption of these technologies in the pharmaceutical sector.