Alessandra Somma, Alessandra De Benedictis, Christian Esposito, Nicola Mazzocca
In recent years, the emerging Digital Twin (DT) technology is playing a key role in fostering the transition towards the Industry 4.0. DTs, representing virtual replicas of physical objects, products or processes established thanks to a bidirectional continuous flow of information between the physical and the virtual world, are currently adopted in multiple domains such as manufacturing, aerospace, automotive, energy, construction, smart cities and smart mobility, etc.. DTs live together with the physical system they replicate, receiving the same data and often triggering specific control actions that directly impact on the real system status. When dealing with DTs of complex Cyber-Physical Systems (CPSs), the data sources may be heterogeneous and untrustworthy, which requires the DT to be able to address security issues related to data in transit from/to the physical twin and data at rest. A possible way to address these data security issues consists in adopting Distributed Ledger Technologies (DLTs) which provide several security guarantees on data through cryptographic hashing techniques. In this work, we present a three-fold contribution: (i) we discuss the results of a Systematic Literature Review (SLR) on the state-of-the-art of the research related to the integration between DLT and Digital Twins, aimed at clarifying relevant aspects such as, among others, what is the favourite DLT choice in existing proposals or what is the type of DT-related information to store on-chain; (ii) leveraging the SLR results and other related research, we propose an architectural framework for the integration of DT and DLTs (more specifically, blockchains); (iii) we validate the proposed architecture by means of two proof-of-concept implementations leveraging different technological stacks and taking into account two different operational scenarios. Finally, we conduct a requirements coverage analysis and compare the PoCs through a coverage matrix.
This research introduces a decentralized supply chain optimization model that incorporates blockchain technology. The model, implemented through an optimized iterative method, integrates ordering, holding, and purchasing costs to offer a comprehensive view of total costs for both retailers and suppliers. The model's uniqueness and optimality are demonstrated through theoretical analysis, highlighting the optimal ordering interval as the sole solution to the derived equation. Employing an algorithmic methodology, optimal replenishment schedules are efficiently calculated using Wolfram Mathematica 13.0. A numerical example and sensitivity analysis illustrate the impact of key parameters on replenishment cycles, order quantity, and costs, encompassing wholesale prices, demand uncertainty, and holding/ordering costs. Managerial insights derived from sensitivity analysis guide decision-makers in optimizing supply chain management, emphasizing strategies such as wholesaler price balance and strategic blockchain information management. In essence, this research contributes to an enhanced understanding of decentralized supply chain models with blockchain, providing a systematic decisionmaking optimization approach for increased efficiency and resilience.
This chapter explores the dynamic interaction between blockchain technology and digital twins in the context of green investing. It examines the revolutionary influence of the synergy of these cutting-edge technologies on the efficiency, transparency, and dependability of sustainable investing methods. The chapter begins with an informative introduction to the fundamentals of blockchain and digital twins, illuminating their separate functions before revealing their joint potential for strengthening green investment initiatives. A detailed assessment of the most recent improvements in blockchain and digital twin technologies follows, focusing on smart contracts, enhanced data analytics, and real-time monitoring. The actual implementation and effects of this integration are presented via illustrative case studies of renewable energy, sustainable infrastructure, and environmental conservation. This chapter delves deeper into the problems of integrating these technologies, providing practical insights and solutions to technical, regulatory, and operational roadblocks.
The rapid evolution of transformative technologies, including blockchain and digital twins, is reshaping numerous industries. Blockchain, recognized for its distributed ledger capabilities, is gaining widespread acceptance owing to its secure and transparent data management features. Simultaneously, digital twins, virtual replicas of physical assets, are proving instrumental in offering real-time insights and enabling remote monitoring and control. This chapter explores the convergence of these two technologies, emphasizing their potential in the real world. Commencing with an overview of blockchain and digital twins, the chapter proceeds to scrutinize their architecture and consensus mechanisms. Subsequently, it delves into the concept of digital twins and their versatile applications across various sectors. Finally, it articulates the advantages derived from the integration of blockchain and digital twins.
Supply chain traceability is essential for ensuring safety, preventing counterfeit goods, and improving efficiency. The integration of blockchain technology and the Internet of Things (IoT) has emerged as a transformative approach to enhance supply chain traceability by creating a secure, transparent, and efficient way to track the movement of goods and materials. This comprehensive literature review examines how the integration of blockchain and the Internet of Things can enhance supply chain traceability, utilizing a systematic literature search to identify and analyze all relevant studies. Recent and related articles selected from the Scopus database were reviewed. Our analysis underscores the potential for blockchain and IoT integration to provide end-to-end visibility, secure data sharing, and real-time monitoring across the supply chain ecosystem. It also identifies Machine Learning (ML) as another key component that enhances the security challenges of the Internet of Things while simultaneously serving as an analytical tool in Supply Chain Management (SCM). The review concludes that the integration of blockchain, the Internet of Things, and ML has the potential to transform supply chain traceability. By providing a secure, transparent, and efficient way to track the movement of goods and materials, businesses can improve their operations and offer better products and services to their customers. However, these findings do not impact the results of this research work. Additional research and a more extensive examination of the literature could offer a more comprehensive insight into the subject matter.
Mifta Ahmed Umer, Elefelious Getachew Belay, Luís Borges Gouveia
Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.
A smart factory is an advanced manufacturing system that utilizes various cutting-edge technologies such as IIoT (Industrial Internet of Things), big data, AI(Artificial Intelligence), blockchain to automate and optimize production processes. While there is a growing demand for smart factories in recent times, the adoption and proliferation of these facilities have been delayed due to concerns about security, the reliability of collected information, and challenges in management and control. Moreover, traditional centralized smart factory systems pose a risk of operational downtime in the event of failures or attacks since a central server controls the entire system. Therefore, we propose the design of a secure, transparent, and reliable blockchain-based IIoT framework for smart factories. The framework consists of three layers: the blockchain core layer, the blockchain operation layer, and the IIoT service layer. The IIoT service layer plays a crucial role in providing various services essential for the advancement of smart factories, utilizing blockchain technology. Our proposed framework combines IIoT and blockchain technologies to leverage the advantages of decentralization, trust, security, transparency, data management, and traceability.
In the context of blockchain technology and cryptocurrencies, computer graphics integration seeks to improve data visualization and user interfaces, offering intuitive and aesthetically pleasing experiences. This entails developing immersive user interfaces for decentralized apps, strengthening security using visual cryptography, and increasing accessibility to complicated blockchain data. Visualizations help users comprehend transaction patterns and system efficiency by making blockchain data easier to analyze. The ultimate goal of this strategy is to improve the usability and engagement of interactions with digital assets and blockchain technology. Index Terms: Blockchain technology, cryptocurrency, data visualization, Computer Graphics, Supply chain, finance.
Industry 4.0 has revolutionized manufacturing processes and facilities through the creation of smart and sustainable production facilities. Blockchain technology (BCT) has emerged as an invaluable asset within Industrial Revolution 4.0 (IR4.0), offering increased transparency, security, and traceability across supply chains. This systematic literature review explores the role of BCT in creating smart and sustainable manufacturing facilities, while exploring its implications for supply chain management (SCM). Through a detailed examination of 82 research articles, this review highlights three areas where BCT can have a dramatic effect on smart and sustainable manufacturing: firstly, BCT can promote green production methods by supporting efficient resource use, waste reduction strategies and eco-friendly production methods; and secondly, it allows companies to implement smart and eco-friendly manufacturing practices through BCT solutions. BCT promotes intelligent manufacturing systems by facilitating real-time data sharing, predictive maintenance, and automated decision-making. Furthermore, BCT strengthens SCM by increasing visibility, traceability, and collaboration between partners of SC operations. The review also highlights the potential limitations of BCT, such as scalability challenges and the need for standardized protocols. Future research should focus on addressing these limitations and further exploring the potential of BCT in IR4.0.
Yung Po Tsang, Youqing Fan, C.K.M. Lee, H.C.W. Lau
In today’s competitive and volatile e-commerce markets, effective supply chain performance measurement (SCPM) gradually becomes essential for investigating pitfalls and loopholes in strategic vendor management and business development. Surrounding the atmosphere of Industry 5.0, sustainability and resilience are two primary pillars, which should be further considered in the SCPM beyond merely operational effectiveness and efficiency. This study customises evaluation criteria based on the green supply chain operations reference model. Moreover, a blockchain sharding-enabled performance analytics system is designed, where a real-life case analysis is conducted to verify the system efficacy. Consequently, the supply chain sustainability and resilience can be fostered.
Several studies were conducted to demonstrate the application of Process Mining (PM) techniques to Ethereum-compatible application event data. However, the availability of event data is constrained by the application’s process awareness, which is under-reported in the literature. Based on domain analysis, which identified several challenges to mining the business process from blockchain applications, a framework was designed, instantiated, and tested in this study. The framework supports identification of appropriate cases for PM and automates the generation of event logs from blockchain data. It consists of two modules, the Process Awareness Recognizer (PAR) and the Event Log Generator (ELG). PAR is a rule-based classifier to assess the process awareness of a given application. ELG is an automated batch processing model consisting of three methods: (1) Extractor: to retrieve event data from blockchains; (2) Decoder: to transform the extracted data to a human-readable format; and (3) Formatter: to produce event log files in a format compatible with PM tools. It was validated by implementing a proof-of-concept application with an input set of 201 real-world applications. The results prove the framework’s feasibility and applicability.
Since Manufacturing 4.0 faces various challenges, including the risks of data leakage and privacy violation, the struggle to meet the growing demand for personalization, and the limitations in harnessing human creativity, it has become crucial to embark on a transformation toward Manufacturing 5.0. To this end, we propose a DeFACT framework for parallel manufacturing and Manufacturing 5.0, which focuses on safe, efficient and personalized collaborative production. In DeFACT, different enterprises and parallel workers (i.e., digital, robotic and biological workers) are organized, coordinated and scheduled based on decentralized autonomous organizations and operations to promote mutual benefits among members, even in the context of low or zero trust. This contributes to providing customers with higher-quality personalized products and services while ensuring the confidentiality and safeguarding of data. Additionally, various advanced technologies, such as generative artificial intelligence, scenarios engineering, and blockchain, are leveraged to achieve trustworthy and adaptable decision making, user-friendly human–machine interaction, and the federated control and management of parallel workers. Finally, the effectiveness and efficiency of DeFACT are experimentally validated through the design and implementation of three case studies.
The development of industry 4.0 relies on emerging technologies of digital twin, machine learning, blockchain and Internet of Things (IoT) to build autonomous self-configuring systems that maximize manufactory efficiency, precision and accuracy. In this paper, we propose a new distributed and secure digital twin driven IIoT framework that integrates federated learning and Directed Acyclic Graph (DAG) blockchain with sharding. The proposed framework includes three planes: the data plane, the blockchain plane and the digital twin plane. Specifically, the data plane performs federated learning through a set of cluster heads to train models at network edges for twin model construction. The blockchain plane, which supports sharding, utilizes a hierarchical consensus scheme based on DAG blockchain to verify both local model updates and global model updates. The digital twin plane is responsible for constructing and maintaining twin model. Then, an efficient resource scheduling scheme is designed by considering performance of both federated learning and DAG blockchain with sharding. Accordingly, an optimization problem is formulated to maximize long-term utility of the digital twin driven IIoT. To cope with mapping error in the digital twin plane, a multi-agent Proximal Policy Optimization (MAPPO) approach is developed to solve the optimization problem. Numerical results illustrate that comparing with traditional approach, the proposed MAPPO improves utility by about 37 %, and reduces time latency by about 14%. Moreover, it also can well adapt to the mapping error.
The transformation of dynamic supply chains within the realm of offsite manufacturing presents a unique and compelling opportunity to effectively respond to shifts in market demand. However, this paradigm shift also entails novel challenges and prospects for streamlining processes, augmenting efficiency, and ultimately driving success across production workflows and logistical operations. Dynamic supply chains necessitate unimpeded access to high-quality, dependable data to facilitate real-time, data-driven decision-making. Furthermore, owing to their inherently intricate nature, these supply chains are subject to a heightened vulnerability to disruptions and delays, surpassing those encountered in traditional supply chains. Against this backdrop, this paper proposes a framework that synthesises the convergence of the Internet of Things (IoT), Building Information Modelling (BIM), and blockchain technology (BCT) into a cohesive and integrated system, thereby optimising the efficiency and transparency of supply chain management operations. The proposed framework takes cognisance of the challenges inherent in ensuring information traceability, mitigating fragmented information, enhancing interoperability, and fortifying reliability throughout the design, production, and implementation phases of assets within dynamic supply chains operating in the offsite manufacturing domain. To illustrate the efficacy of our proposition, we present a framework that exemplifies the seamless integration of a permissioned, open-source distributed ledger, which forms the bedrock of our support infrastructure for IoT and BIM integration. Through rigorous analysis, we showcase the effectiveness of the developed framework in the form of a designed Supply Chain Management Model (SCMM-PDO), which harmonises the physical, digital, and operational layers within a robust system architecture (BCT.IoT-BIM). The integration of these technologies provides a decentralised solution, reinforcing supply chains with improved security, reliability, and quality control mechanisms, thus enhancing the efficiency of the supply chain network. It is anticipated that this transformative integration will yield substantial improvements in key performance indicators (KPIs) such as operational efficiency, cost reduction, and the cultivation of trust and accountability across supply chain activities. The research proposes an IoT and BIM conceptual framework that simulates the dynamic supply chain operations, facilitating real-time analysis and prediction of supply chain participants' behaviours from resources, processes, and assets. Furthermore, the integration of IoT-BIM (digital twinning) with a blockchain-based system, such as Hyperledger Fabric, engenders a secure and transparent framework for the diligent tracking of assets and information throughout the supply chain. The findings of this study offer an all-encompassing conceptual framework for the secure and transparent tracking of asset information by integrating Blockchain-based technologies with IoT and BIM. This research endeavour thus expands the existing body of knowledge, establishing a foundation for future investigations within this proliferating field.
The convergence of Web3, Metaverse, and Digital Twins technologies is bringing a transformative revolution to Cyber-Physical-Social systems. Web3, which is driven by blockchain and decentralization, allows users to have control over their data and digital assets. Meanwhile, the Metaverse is creating a virtual space where people can interact, work, and live together. Digital twins offer a real-time digital representation of physical objects or spaces. When these three concepts intersect, they create a dynamic and interconnected digital ecosystem where the physical and virtual worlds blend seamlessly. This paper focuses on discussing the convergence of Metaverse, Web3 technologies, and Digital Twin. We will focus on the architecture of a Web3-enabled Metaverse, which leverages the decentralized nature of Web3 to offer a distributed Metaverse. The digital twin technology will realize a Cyber-Physical-Social data binding that enables the seamless mapping of physical and social activities into cyberspace. Finally, we will discuss the challenges of a Web3-enabled Metaverse, such as security, privacy, interoperability, and ethical concerns.
This chapter compares how blockchain, the Internet of Things (IoT), and artificial intelligence (AI) technologies are used in supply chain to promote sustainability. The chapter starts with explaining the concept of supply chains and their increasing importance in today’s business world. It explores the challenges and objectives involved in managing supply chains, which sets the foundation for examining how blockchain, IoT and AI contribute to this transformative approach. The study extensively analyses how blockchain advances the sustainability of supply chains. It provides insights into real-world implementations and evaluates their social impacts. Additionally, it explores applications of IoT that utilize sensor technology for monitoring conditions, tracking assets, and reducing waste within supply chains. The study also investigates how AI contributes to sustainability through activities like demand forecasting, optimization, resource management, predictive maintenance, and energy conservation. This research highlights the potential of blockchain, IoT, and AI in advancing supply chain management. It offers a nuanced understanding of these technologies’ strengths and weaknesses plus their cost effectiveness and adaptability in supply chain. It draws on real life examples from organizations that have successfully utilized these technologies to improve integration opportunities and overcome challenges. This research contributes to the understanding of the critical role played by blockchain, IoT, and AI technologies for the achievement of sustainability goals within supply chains. It offers practical recommendations for researchers, policymakers, and supply chain practitioners, enabling them to optimize the use of technologies in achieving sustainable supply chains. In conclusion, this study predicts the trends in integration of technology for supply chain sustainability.
Claudia Pipino, Gaetano Rocco, Claudio Pagano, Andrea Cipriano
Small and Midsize Enterprises (SMEs) have faced innovative changes in their organizational structure, especially in human resources (HR) and employee management: these digital structural changes have transformed their business practices and strategies. The employees with new technical skills have also been impactful in this digitalization. The HR departments in SMEs have become increasingly pivotal in recruiting new employees and supporting existing employees throughout the organization. ITSvil in Salerno (Italy) has developed the Easywork project based on the “Fenice” microservices architecture. This system supports the HR departments in efficiently managing HR activities through digitalization. The first step in acquiring new resources is formulating the employment contract, which is essential for stipulating agreements on specific job requirements. Blockchain technology and smart contracts are among recent technological innovations impacting this process. Writing contracts in digital codes to simplify and effectively manage the legal relationship involving bureaucratic tasks and cost management is now possible. The new digital system makes employment contract processing transparent, traceable, and reliable. This research presents the design and development of a new microservice compatible and integrable process with the “Fenice” system based on blockchain technology. The proposed digital system aims to make HR management activities more transparent and traceable.
Ahsan Waqar, Lubna A. Alharbi, Faiz Abdullah Alotaibi, Khaled A Alrasheed · 6 authors
The implementation of blockchain techniques in extensive building work undertakings has encountered multiple obstacles. The barriers encompass a variety of areas including technical, functional, economic, social, and standardization concerns. Notwithstanding the growing recognition of the prospective advantages of blockchain technology among the building industry, there exists a paucity of the literature that delves into the obstacles of implementing blockchain technology in extensive building undertakings. The purpose of this study is to check the difficulties attached with the implementation of blockchain technology in extensive building undertakings. This research study uses a mixed‐method technique that combines structural equation modelling (SEM) and exploratory factor analysis (EFA) to assess the data gathered from a survey of construction industry experts. The conclusions of this research study recommend that the implementation of blockchain technology in large‐scale building projects is significantly impacted by various barriers, including procedural, technical, functional, standardization, social, and economic factors. This research study reveals that the primary obstacles to standardization, technical integration, and functional implementation are the absence of established norms, barriers to assimilating with current systems, and restricted utilization scenarios, respectively. Furthermore, this research study has revealed that the primary, social, and economic hurdles for implementation of blockchain technology in extensive building projects are stakeholders’ reluctance to change, disputes over ownership, and the challenge of demonstrating a definitive return on investment. The study’s implications indicate that the building industry should establish a uniform methodology for implementing blockchain technology that effectively tackles the barriers brought to light in this study. This study’s results provide great views to different stakeholders in the building industry, such as construction managers, investors, and regulators, regarding the difficulties involved in implementing blockchain technology in extensive building projects. The research implications underscore the necessity for a standardized approach to implement blockchain technology in large‐scale construction projects. This insight is crucial for construction professionals, investors, and regulators, informing strategic decisions to overcome identified barriers and facilitate the seamless integration of blockchain, thereby enhancing efficiency and transparency in the industry.
The dramatic development of technology in recent years has affected most organizations and companies. Artificial intelligence (AI) and Blockchain can be mentioned as the most important technologies that are developing rapidly. In Industry 4.0, complex data are produced in high volume, which makes implementing improvement projects with traditional methods lose their effectiveness. Six Sigma is one of the most prominent improvement methods organizations and companies use to identify and solve problems. Therefore, for implementing Six Sigma projects in Industry 4.0, the need to develop the traditional Six Sigma toolbox is felt. AI and Blockchain can be suitable tools for developing and improving Six Sigma for Industry 4.0. A systematic review identified 58 articles in this article that presented solutions for integrating AI or Blockchain in Six Sigma. Some articles have evaluated the performance of their proposed method by implementing the proposed models. The most widely used machine learning and deep learning algorithms in Six Sigma have been identified. Also, Six Sigma approaches that mostly use AI or Blockchain have been identified by the analysis of articles. Decision tree algorithms and artificial neural networks are used in most Six Sigma define–measure–analyze–improve–control (DMAIC) projects. Therefore, by reviewing the articles, it was found that AI and Blockchain are mainly used as efficient tools in DMAIC and the Design for Six Sigma models to implement Six Sigma projects. In this article, 28 main gaps are presented as future works for future research.
991013340354003412 HKUST Electronic Theses Defect detection for smart contracts and blockchain applications by Wuqi Zhang thesis 2024 1 online resource (xix, 129 pages) : illustrations Blockchain technology has attracted significant interest with the advent of Turing-complete smart contracts, which enable…Read more ›
Paula Fraga‐Lamas, Tiago M. Fernández‐Caramés, António Miguel Rosado da Cruz, Sérgio Ivan Lopes
Industry 5.0 is an evolving concept that aims to enhance the way modern factories operate by seeking long-term growth, production efficiency and the well-being of industrial workers. Human-centricity, sustainability and resilience are the three pillars of Industry 5.0, which are developed on Industry 4.0 enabling technologies. One of the most compelling technologies to help implement the communications architecture proposed by Industry 5.0 is blockchain, which can provide trustworthy, secured and decentralized information to different industrial domains. This article provides an analysis of the transition between Industry 4.0 and Industry 5.0 paradigms. Moreover, it examines the benefits and challenges that arise when using blockchain to develop Industry 5.0 applications and analyzes the design factors that should be considered when developing this type of applications. Furthermore, it presents a thorough review on the most relevant blockchain-based applications for Industry 5.0 pillars. Therefore, the main goal of this article is to provide a comprehensive and detailed guide for future Industry 5.0 developers that allows for determining how blockchain might benefit the next generation of human-centric, sustainable, and resilient applications.