This article explores the impacts of digital transformations and new technologies in industrial sector (particularly through the Fourth Industrial Revolution) on optimizing production processes. Characterized by key technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), blockchains, and advanced robotics, Industry 4.0 has significantly shaped modern manufacturing management. IoT enables autonomous communications between machines and equipment, providing real-time insights into production parameters and enabling predictive maintenance, and big data plays a vital role by analyzing the large volumes of data that are generated by these devices, thus supporting informed management decisions. AI and machine learning help automate complex tasks, optimize production schedules, and improve product quality through real-time adjustments. Blockchain enables decentralized and secure data recording, which is particularly useful in supply-chain management. Advanced robotics increases production speed and accuracy, thus reducing labor costs and mitigating any risks that are associated with hazardous tasks. Integrating these technologies requires strategic planning, including identifying key challenges, conducting pilot projects, integrating with existing IT and OT systems, and managing organizational change. Measuring the effectiveness of Industry 4.0 implementation should involve well-defined key performance indicators (KPIs) and return-on-investment (ROI) analysis. The primary challenges that are associated with adopting Industry 4.0 include the alignment of technology with specific business needs, employee resistance to change, and hidden costs of implementation. In summary, industrial transformation offers opportunities for companies to optimize production processes, reduce costs, and increase competitiveness in the global marketplace. However, a careful approach is necessary to maximize efficiency, foster innovation, and secure long-term success in an increasingly digitalized world.
This article explores the integration and innovation of NFT (Non-Fungible Token) and digital twin technologies within the realm of metaverse architecture. As the construction industry faces challenges such as limited resources, rising costs, and environmental pressures, the metaverse offers a new direction through digital modeling and twin representation technologies. These technologies are applied throughout the entire lifecycle of architectural projects, providing fundamental technical support and practical experience for the realization of metaverse architecture. The article highlights the unique features of metaverse architecture, such as virtuality, interactivity, and infinite possibilities, which allow for greater design freedom and new creative platforms for architects. The fusion of NFT and digital twin technologies brings new business models and market opportunities to the construction industry. The research method involves case studies, including the Mars House as an example of NFT architecture and the digital twin application of Beijing CBD. The main results show that this technological integration can enhance design efficiency, optimize asset management, and promote innovation and collaboration in the construction industry.
Marta Rinaldi, Mario Caterino, Stefano Riemma, Roberto Macchiaroli · 5 authors
Background: Emergency scenarios present unprecedented challenges for supply chains worldwide, particularly in the management and distribution of critical supplies, where timely delivery and maintaining integrity are crucial. Methods: This article explores an innovative approach to enhance the emergency management of supply chains using blockchain technology and simulation-based modelling. The proposed methodology aims to tackle issues such as transparency, efficiency, and security, which are vital for managing logistics during crises. A case study involving a vaccine rollout is used to demonstrate how blockchain can optimise supply chain operations, reduce bottlenecks, and ensure better traceability and accountability throughout the process. The case study is specifically developed based on the distribution of COVID-19 vaccines in Italy. Results: The integration of blockchain technology not only enhances data integrity and security but also facilitates real-time monitoring and decision-making. Conslusions: The findings suggest that the proposed blockchain-based model can significantly improve supply chain resilience in emergency situations compared to traditional methods, thereby offering valuable insights for policymakers and supply chain managers facing future crises.
By drawing from the Technology-Organization-Environment (TOE) theory, this research aims to develop a model to examine the adoption of a blockchain technology based on a survey from 362 manufacturers in Sichuan, China. A structural equation modeling method was adopted to analyze the effects of the TOE approach on the manufacturer’s adoption of blockchain technology. The results indicated that technology, organization, and the environment had positive effects on the manufacturer’s adoption of blockchain technology. Organizational factors are significant antecedents to a manufacturer’s adoption of blockchain technology, followed by technological and environmental factors. Specifically, this research found that environmental factors had a moderating effect and strengthened the influence of organizational factors on the manufacturer’s adoption of blockchain technology. Managerial and theoretical implications from the research findings were discussed to develop blockchain technology in the manufacturing industry.
Nuzhat Noor Islam Prova, Nikhil Kassetty, Vishal Narender Punjabi, Sandeep Keshetti · 5 authors
The quick globalization of supply chain management (SCM) has resulted in increasingly complicated networks, and there is a need for transparency, efficiency, and security advancements. This research proposes an innovative framework that addresses these challenges and integrates blockchain-powered smart contracts, IoT-enabled real-time data tracking, and a robust Management Information System (MIS) dashboard. Developed on Ethereum’s decentralized architecture, this model provides a transparent and efficient environment for stakeholders and automates essential processes, including payment triggers, compliance with quality criteria and logistics support. Advanced features like demand-supply smoothing algorithms, blockchain-anchored robotic process automation (RPA), AI-driven risk analysis, digital twin systems, and multi-layer privacy protection further enhance the framework. IoT sensors also enable monitoring sustainability in real-time, while gamification and blockchain oracles guarantee stakeholder participation and dynamic optimization. The presented method addresses the main issues with SCM by reducing fraud, enhancing operational timeliness, providing regulatory compliance, sophisticated IoT-AI synergies and leveraging Layer2 solutions for scalability. This research highlights the transformative possibility of blockchain smart contracts in revolutionizing SCM by creating a secure, transparent, and sustainable supply chain ecosystem.
Abdullah Lakhan, Zaid Abdi Alkareem Alyasseri, Mazin Abed Mohammed, Bourair Al-Attar · 8 authors
In this era, digital technologies such as artificial intelligence, the Internet of Things (IoT) and blockchain are gaining popularity in research and academia. The supply chain management application is the key to achieving many benefits from AIoT and blockchain technology. However, these technologies have many issues, such as sustainability, a green environment, and multiconstraints (e.g., time, energy, cost, and CO2) for supply chain management applications. This article presents sustainable, secure blockchain-assisted AIoT and green multiconstraint supply chain systems. Initially, we present a secure and sustainable methodology that securely validates the supply chain management system data. For the green environment, we consider the problem a combinatorial problem consisting of different constraints such as time, energy, cost, and carbon dioxide (CO2). To solve this problem for supply chain management jobs, we present a multiconstraint genetic algorithm deep convolutional neural network (MCGA-DCNN) algorithm methodology. The objective is to reduce total processing time, total processing energy consumption, cost, and the CO2 environment as a green environment for supply chain management jobs. The genetic algorithm is evolutionary, where the fitness function optimizes the multiconstraint weights at the runtime based on DCNN and provides the optimal solutions for jobs. Simulation results show that MCGA-DCNN minimized the time, energy, cost, and CO2 and securely validated all transactions for all supply chain management jobs compared to existing schemes.
This chapter explores the advancements in human-machine interfaces (HMIs) and their potential to enhance user experience and interaction in information applications, particularly within Web3 environments. As decentralized technologies like blockchain and dApps grow, interface design becomes crucial for improving user engagement and usability. The paper examines the latest developments in HMIs, such as natural language processing, gesture recognition, augmented reality (AR), and virtual reality (VR), and their impact on Web3 applications. These technologies offer innovative ways to enhance user experience by providing more intuitive and immersive interactions in decentralized systems. Additionally, the paper discusses the challenges of implementing advanced HMIs in Web3, including issues of compatibility with blockchain protocols and the need for seamless user experiences.
The evolution of supply chain analytics has been accelerated by digital transformation, with Digital Twins, the Internet of Things (IoT), and Blockchain emerging as pivotal technologies for creating resilient, data-driven business operations. Traditional supply chains face challenges such as demand fluctuations, logistical inefficiencies, supply disruptions, and lack of real-time visibility. Advanced supply chain analytics, powered by Artificial Intelligence (AI) and big data, enables organizations to transition from reactive to predictive and prescriptive decision-making, optimizing efficiency and mitigating risks. Digital Twins facilitate real-time simulations of physical supply chain processes, allowing businesses to model disruptions, optimize inventory, and improve logistics planning before implementing changes in the physical world. IoT-enabled devices, such as smart sensors and RFID trackers, provide continuous monitoring of goods in transit, ensuring visibility into factors like temperature, location, and shipment integrity. Meanwhile, Blockchain technology enhances supply chain transparency, traceability, and security, enabling secure data sharing and fraud prevention in multi-stakeholder ecosystems. These technologies collectively enable organizations to achieve real-time supply chain optimization, proactive risk management, and sustainable operations. However, challenges such as data standardization, integration complexity, and cybersecurity risks must be addressed for seamless implementation. This paper examines the methodologies, industry applications, and future potential of advanced supply chain analytics, providing strategic insights into how businesses can leverage Digital Twins, IoT, and Blockchain to enhance agility, reduce operational costs, and build resilient global supply networks.
Mohammad Yaser Mofatteh, Ujjwal Khadka, Omid Fatahi Valilai
Energy management can be designed from different perspectives including production, distribution, and consumption. Focusing on consumption perspective, manufacturing systems can be enhanced by enabling smart machines as agents which operate with their own knowledge representation models in a shopfloor. These agents can benefit from industry 4.0 enablers like IoT including sensors, controllers, and actuators. This paper focuses on how these agents can interoperate with each other and exchange knowledge to optimize energy consumption. Since different knowledge models may not be capable of interacting with other ones based on their different provider semantics. This paper explores the application of blockchain technology for secure, decentralized storage and sharing knowledge models in smart energy systems. The research introduces EnerChain as a blockchain-integrated and a decentralized application (DApp) system prototype that employs smart contracts for access management and conflict resolution. It also incorporates the InterPlanetary File System (IPFS) for efficient off-chain storage, addressing scalability concerns. The feasibility and practicality of this approach are demonstrated through the development of EnerChain. The findings highlight the significant potential of blockchain technology in facilitating efficient knowledge model management for smart shopfloors. Additionally, an operational scenario has been evaluated as a case study for the proposed conceptual model to illustrate how it can solve energy conflicts in a smart environment. An impact analysis at the end of this research shows that EnerChain can make annual 27.5 TWh reduction in residential energy consumption which yields to annual 7.8 million tonnes reduction in CO 2 emissions and annual €8.25 billion financial benefits.
Judith Nkechinyere Njoku, Cosmas Ifeanyi Nwakanma, Jae‐Min Lee, Dong‐Seong Kim
This study presents a digital twin framework for predicting the state of health (SoH) in battery management systems (BMS). This framework integrates a single particle model with electrolytes (SPME) and a long-short-term memory (LSTM) network to model battery behaviour based on a NASA battery dataset. To ensure the security of battery data, data is recorded on the Ethereum blockchain and queried when needed for secure prediction. To ensure the interpretability of the predictions, an explainable AI (XAI) approach, SHAP, is employed. Experimentation shows the viability of the proposed framework in accurately predicting the SoH of physical batteries.
Digital product passports (DPPs) will become a reality for several regulated products in Europe. The topic is still in its infancy but will significantly impact product information and the infrastructure required by manufacturing in increasingly agile and circular supply chains. This paper presents the results of a two-year design science research to develop an end-to-end blockchain-based DPP prototype instantiated in the textile industry. On the one hand, the upstream supply chain involves physical product transformations from the early stages of production, requiring a robust traceability architecture. On the other hand, multiple events occur during downstream phases that need to be easily accessible by different stakeholders. Our results confirm the suitability of blockchain-based DPPs and define the information flow within the product life cycle. This paper advances the literature on sustainable product identification, ensuring tamper-proof and transparent information in the supply chain. Although aiming at the end consumers, the DPP will majorly impact production, requiring proper industry planning. For practitioners, this paper provides one of the first DPP instantiations in different segments of the textile supply chain, highlighting the requirements that the industry should be aware of for the coming years. Eleven design principles for blockchain-based DPPs are proposed.
The integration of blockchain and AI technologies in digital twin–based smart grids harnesses the vast potentials lying ahead. This ensures heightened security, privacy, and operational effectiveness within energy systems. A digital twin is a detailed virtual replica of physical power systems, with real-time monitoring, predictive maintenance, and scenario simulation. Furthermore, it guarantees data integrity and transparency through its decentralized immutable ledger and secure automated transactions using smart contracts. This synergy enhances grid resilience, optimizes resource management, and builds trust with stakeholders through data protection and the operational transparency it provides around sensitive data. However, these technologies also raise significant security and privacy challenges when deployed. Depending on AI and blockchain, the smart grid becomes exposed to new cyber threats, including man-in-the-middle and distributed denial of service attacks, which compromise data integrity and system integrity. This is ensured by sensitive information being protected at all costs, including consumer energy use patterns and details of grid infrastructures. It could be ensured via implementation of advanced encryption techniques and strong access control mechanisms to prevent unauthorized access and breaches. In addition, adherence to regulatory compliance, such as GDPR, CCPA, and data protection laws, strictly enforces data governance and proper anonymity practices to follow in building consumer trust and remaining legally compliant.
In the following paper, the possibilities of the latest industry 4.0 and digitization technologies, such as the use of Blockchain (BC) in Model-Based Systems Engineering with the associated product data (PDM) and product life cycle (PLM) management information, will be considered holistically. A Blockchain Engineering approach called EngineeringBlock will be researched in Systems engineering for the management of software data and engineering data within product lifecycle management. At this point, data input refers, for example, to the uploading of software versions to a control unit in the Engineering Data like Requirements and Functions. The two most important directions of the research paper will show the use of Blockchain technology with the related PLM System. This paper will present a Framework which enables the presentation of all product-relevant requirements, functions and logical aspects of the product up to the usage phase in a holistic and consistent way.
Manufacturing sectors pursuing Industry 5.0 objectives increasingly require automation architectures capable of reasoning across heterogeneous cyber-physical domains while advancing sustainability targets. This paper synthesizes evidence from eighteen references spanning semantic web technologies, cyber-physical systems, digital twins, blockchain-enabled traceability, and multi-agent reinforcement learning to propose a Semantic AI-Orchestrated Cross-Domain Automation Framework for sustainable smart factories. The framework integrates a five-layer architecture, comprising physical sensing, cyber-physical integration, semantic reasoning, cross-domain orchestration, and sustainability decision layers, into a unified reasoning pipeline in which ontology-driven knowledge graphs mediate interoperability among heterogeneous equipment, enterprise systems, and human operators. Comparative synthesis indicates that semantic-based autonomous computing architectures reduce unplanned downtime by as much as 37 percent, while multi-agent reinforcement learning scheduling raises resource utilization to 88 percent relative to 58 percent under conventional rule-based scheduling. Interoperability standards such as OPC-UA demonstrate adoption rates near 78 percent among reviewed implementations, and hybrid semantic-blockchain ledgers achieve transaction throughput exceeding 2,100 transactions per second at latencies below 40 seconds, outperforming public proof-of-work ledgers by more than two orders of magnitude. Digital twin adoption trajectories synthesized from the references rose from approximately 8 percent in 2016 to 66 percent by 2024, correlating with a 24 percent reduction in energy consumption and a 31 percent reduction in material waste across reviewed sustainable manufacturing cases. The findings imply that semantic orchestration, combined with decentralized ledgers and human-centric digital twins, offers a scalable pathway toward resilient, low-carbon, and economically viable smart factory operations, while highlighting persistent challenges in ontology standardization, explainability, and cross-organizational governance.
Non-fungible tokens (NFT) are secured storage methods for unique and non-interchangeable data items. While NFTs are commonly associated with digital arts and the cryptocurrency market, the paper explores their potential in industrial applications, particularly within operations and supply chain management. The paper introduces the technology and key processes of NFTs, providing a foundation for understanding their application in operations and supply chains. The design science method is employed by conducting requirement collection with a small group of experts and based on this a set of potential applications for operations and supply chain management is outlined. Five use case applications are presented from the requirements management point of view. The artefact developed in the process is a sample implementation, demonstrating how NFTs can be practically employed to address specific operational requirements. The results indicate that physical components, digital assets, uniqueness of the entity, contracts, security, and multiple stakeholders collectively enable various use cases where NFTs can provide value.
This chapter describes how a number of right technologies are coming together at the right time to enable the development of Web3 and the metaverse. It will give an overview of the roles of major emerging technologies such as 5G and 6G cellular networks, artificial intelligence (AI) (including generative AI), blockchain, the Internet of things (IoT), and extended reality (XR) devices, which include augmented reality (AR), virtual reality (VR), mixed reality (MR), and digital twin in building the metaverse and Web3. The chapter evaluates how these technologies have facilitated the uptake, scale-up, and deployment of Web3 and the metaverse. As a key technology behind Web3 and the metaverse, it promotes an understanding of the roles of blockchain in keeping distributed ledgers of all transactions and creating shared and immutable records, which help ensure transparency and security and create “rustless” digital relationships and transactions.
In the past few years, "metaverse" and "non-fungible tokens (NFT)" have become buzzwords, and the prices of related assets have exhibited large fluctuations. Are those characteristic of a speculative bubble? In this paper, we attempt to answer this question, and better understand the underlying economic dynamics. We look at Decentraland, a virtual world platform where land parcels are sold as NFT collections. We find that initially, land prices followed traditional real estate pricing models - in particular, value decreased with distance from the most desirable areas - suggesting Decentraland behaved much like a virtual city. However, these real estate pricing models stopped applying when both the metaverse and NFTs gained increased popular attention and enthusiasm in 2021, suggesting a new driving force for the underlying asset prices. At that time, following a substantial rise in NFT market values, short-term holders of multiple parcels began to take major selling positions in the Decentraland market, which hints that, rather than building a metaverse community, early Decentraland investors preferred to cash out when land valuations became inflated. Our analysis also shows that while the majority of buyers are new entrants to the market (many of whom joined during the bubble), liquidity (i.e., parcels) was mostly provided by early adopters selling, which caused stark differences in monetary gains. Early adopters made money - more than 10,000 USD on average per parcel sold - but users who joined later typically made no profit or even incurred losses in the order of 1,000 USD per parcel. Unlike established markets such as financial and real estate markets, newly emergent digital marketplaces are mostly self-regulated. As a result, the significant financial risks we identify indicate a strong need for establishing appropriate standards of business conduct and improving user awareness.
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.
Rommel Velastegui, Raúl Poler, Manuel Díaz‐Madroñero
• Exploration the role of MARS and BCT in reshaping industrial operations. • Proposal of a taxonomy for MARS, BCT and OPC principles. • Discussion of benefits and current limitations for MARS, BCT and OPC integration. This article presents a meticulous literature review that focuses on the integration of blockchain technology (BCT) into multiagent robotic systems (MARS) for operations planning and control (OPC) in industrial settings. By employing a systematic approach involving research question formulation, document exploration, article screening and data analysis, we rigorously examined 276 articles from scientific databases, including WOS, IEEEXplore, and Scopus. By the PRISMA, CIMO, and Cochrane methodologies, we developed a refined selection process and established a clear taxonomy that elucidates the intricate relations among MARS, BCT and OPC based on their objectives, findings, solution methodologies, industry sectors, limitations and benefits. We additionally conducted an in-depth analysis of the current state of the art, by critically evaluating existing research. With this comprehensive review, we answered pertinent research questions by unveiling the profound benefits of applying BCT to MARS in production planning and operations. Notably, this integration enhances data security, ensures the traceability of MARS activities, and significantly improves transparency, efficiency and overall operational robustness in OPC in industrial contexts to provide valuable insights for companies to consider adopting this technology in their industrial operations.