Digital-twin-enabled humanâmachine collaboration (HMC) has increasingly been proposed as a system-level approach for connecting human operators, robots, sensors, artificial intelligence modules, and manufacturing resources. However, the literature varies substantially in what is called a digital twin, how physical and virtual models are coupled, whether models are updated from physical data, and how far systems have progressed beyond simulation or controlled laboratory demonstrations. This structured integrative review examines the conditions under which a digital twin can function as an integration layer for HMC in sustainable smart manufacturing, rather than assuming that such integration is already established industrial practice. The literature corpus was assembled through searches of the Web of Science Core Collection, Scopus, and IEEE Xplore, complemented by Google Scholar-based citation tracking and backward and forward citation tracing. The core search focused on studies published from 1 January 2020 to 5 August 2026, while earlier seminal studies were retained to support definitions and historical context. Studies were screened using explicit criteria for manufacturing relevance, physicalâvirtual coupling, state synchronization or model updating, feedback capability, and validation setting, and were critically coded by model type, integration mechanism, deployment maturity, and sustainability evidence. The review compares multimodal perception and human-state modeling, intention understanding and augmented interaction, task allocation and shared planning, digital-twin architectures, adaptive control and safety verification, and humanâAI decision-making. The evidence indicates that digital twins are promising as coordination and verification layers, but many reported systems remain conceptual, simulation-based, or limited to controlled physical prototypes. Key barriers include model fidelity, online model updating, real-time synchronization, cross-platform interoperability, safety assurance, human-data governance, and the limited availability of directly measured sustainability outcomes. Future work should prioritize validated hybrid models, traceable model-update mechanisms, staged virtual-to-physical deployment, interoperable data contracts, and longitudinal evaluation of technical, human, economic, and environmental performance.
Pharmaceutical supply chain is facing severe problems caused by counterfeiting medicines, unclear tracking system, and inefficient recalling method, which lead to huge economical losses and endanger the public's health. In this paper, we present a novel blockchain-based solution with Non-Fungible Token digital twin (NFT), which is based on Ethereum ERC-1155 standard, to construct an unchanged and transparent record for drug unit's whole lifecycle from manufacturing to final patient. Our system replaces the vulnerable centralized database with a decentralized one to guarantee the integrity of data, automatic compliance and immediate verification. Experimental results on Ethereum Sepolia testnet show that our system achieves 100% success rate for 9 transactions which are 7 transfer transactions and 2 creation transactions with average confirmation time just for 17.8s. The total operation cost for all transactions is only $0.01 USD which is very cost-effective. Base on the comprehensive analysis, our system can save 85-90% of operation cost and improve 95% of recalling time compared with traditional system. The experimental results in this paper prove the practical feasibility and feasibility of economy using NFT digital twin to manage the whole pharmaceutical supply chain, and provide a strong framework to fight against counterfeiting and ensure patient safety.
Tsvetelina Ivanova, L Koleva, Idilia Batchkova, G Kolev
Abstract Reliable vacuum control in high-precision installations such as the Electron Beam Melting and Refining (EBMR) plant requires an intelligent architecture that integrates physical subsystems and cyber entities under an adaptive control framework. This paper proposes a multi-agent system (MAS) representation of the EBMR vacuum creation subsystem, developed using the Organizational Multi-Agent Systems Engineering (O-MaSE) methodology. The model unites the IEC 61512 (S88) batch-process standard with the IEC 61499 distributed-control architecture to form a modular and interoperable cyber-physical system (CPS). Each pump, valve, and sensor is modelled as an autonomous agent with defined goals, roles, and communication protocols. The O-MaSE-based design enhances scalability, fault tolerance, and system adaptability, enabling decentralized decision-making and efficient vacuum regulation. The integrated case study demonstrates that MAS-based CPS design substantially improves the responsiveness and resilience of EBMR operations, supporting the principles of Industry 4.0.
This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyberâphysical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm.
Mariia Deinega Mariia Deinega, Theodoros Dounas, Daniel Hall Daniel Hall, Hico McDonald Hico McDonald · 6 authors
Decentralized project delivery in architecture faces challenges related to transparency, accountability, and role definition. This paper explores the application of Soulbound Tokens (SBTs) as a governance and record-keeping mechanism within decentralized autonomous organizations (DAOs). Using a systematic review of practice, the paper identifies five opportunities for SBTs, proposes an operating framework for SBTs with respect to record-keeping (e.g., skills, contributions) and project governance (e.g, voting power, reputation), and describes one case of technical implementation of SBTs. Future research can improve on this technical implementation or develop additional decentralised applications for SBT skills verification and governance mechanisms.
The evolving manufacturing landscape, characterized by increasing demands for customization, flexibility, and efficiency, requires adaptive systems that can respond in an autonomous and dynamic way. Bio-inspired Adaptive Manufacturing Systems, or Bio-AMS, provide a novel answer by integrating principles from nature that include self-organization, resilience, and adaptability. This paper covers the development and application of Bio-AMS, in which biological inspiration from organisms and ecosystems was applied to create systems that would adapt to varying production demands, disturbances, and resource constraints. In this manner, Bio-AMS enhances both the efficiency and scalability of systems with decentralized control, adaptive feedback, and MAS architectures. Simulations show that Bio-AMS outperforms conventional systems in terms of minimizing downtime and maximizing productivity. Case studies demonstrate their capability for autonomous adaptation to disruption and therefore operational resilience. Future work focuses on refining the algorithms, integrating advanced technologies like AI and IoT, and taking the applications to various industries.
Ranjit Kannappan, Julien Hatin, E. Bertin, Noël Crespi
The Digital Product Passport (DPP) is a key enabler of the European Unionâs vision for a circular economy. Achieving the full potential of DPP requires addressing the challenges of traditional product lifecycle systems (PLM). Traditional PLM focuses on streamlining data management and decision making. However, their centralized architecture limits transparent, crossorganizational collaboration, impacting the circular economy efforts. This paper proposes a blockchain based framework, tailored to support DPP implementation by enabling the creation and sharing of lifecycle data using digital twin technology. The proposed architecture implements two types of digital twins - Component Digital Twin and Product Digital Twin modeled using the Asset Administration Shell (AAS) standard to ensure interoperability. The architecture leverages Ethereum smart contracts for blockchain interaction and IPFS for off-chain decentralized storage. Two approaches for secure data sharing are implemented: Direct and Signature-based data sharing. Performance evaluation shows low latency for key operations like twin creation (167 ms) and data sharing (64 ms). By leveraging decentralization in DPPs, the proposed framework fosters collaboration, transparency, and circular economy practices, empowering stakeholders to access and share critical product data throughout the lifecycle.
Rong Zhao, Hong Kang, Qiyue Zhang, Sizheng Fan · 5 authors
Blockchain technologies, particularly blockchain-based digital twins (DTs), have gained widespread interest in academia and industry. Despite this, existing literature frequently overlooks the unique characteristics of location information for consensus nodes in DTs, often merging them with conventional blockchain frameworks. This article introduces LocPoS, a novel location-based proof-of-stake mechanism tailored for industrial DTs, bridging this gap. Our proposed model strives to enhance security via broader consensus node distribution. Despite this enhanced security, potential for dishonest behavior by users for gain maximization persists. We perform an in-depth analysis of user strategies and devise a mechanism to inhibit malicious behavior, fostering truthfulness among all users. Our theoretical and simulation results demonstrate that LocPoS satisfies truthfulness and individual rationality, while reducing the proportion of malicious nodes in the consensus group by over 50% compared to PoS and DPoS, thereby significantly improving consensus integrity. The proposed mechanismâs effectiveness in ensuring truthful behavior and secure node selection is validated through both analytical proofs and extensive simulations.
Against the backdrop of the Industry 5.0 transformation, traditional centralized Manufacturing Execution Systems (MES) struggle to meet the complex demands of flexible and small-batch production. This paper proposes a four-level distributed architecture oriented toward human-centric manufacturing, encompassing Manufacturing Units (UMS), Production Lines (PL-MES), Workshops (MOM), and Factories (MOM), supporting dynamic reconfiguration and resource optimization from the unit level to cross-factory operations. Building on this, the study designs a smart factory operating system architecture based on âcloud-edge-endâ collaboration, achieving vertical integration of equipment, systems, and business processes through an industrial internet platform. Leveraging intelligent connectivity gateways and modular microservices, the architecture constructs a decentralized decision-making chain. Innovatively integrating multi-agent systems and federated learning, the architecture significantly enhances the autonomous decision-making capabilities of manufacturing units and cross-level collaboration efficiency. Additionally, the system supports rapid deployment for small and medium-sized enterprises through low-code toolchains, lowering technical barriers. This research provides a theoretical framework and technical pathway for human-machine collaborative manufacturing in the Industry 5.0 era, driving the paradigm shift of manufacturing systems from rigid control to ecological self-organization, laying a solid foundation for the sustainable development of future smart factories
Judith Nkechinyere Njoku, Cosmas Ifeanyi Nwakanma, JaeâMin Lee, DongâSeong 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.
Lean Six Sigma 4.0 (LSS 4.0) represents a transformative evolution of Lean Six Sigma, integrating Industry 4.0 technologies to drive smart manufacturing excellence. By leveraging Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twins, and Big Data Analytics, LSS 4.0 enables realtime decision-making, predictive intelligence, and autonomous process optimization, enhancing efficiency, agility, and resilience in modern industrial environments. This paper introduces a conceptual framework for LSS 4.0, redefining the DMAIC (Define-Measure-Analyze-Improve-Control) methodology through IoT-driven process monitoring, AI-powered predictive analytics, and digital twin simulations. This transformation shifts manufacturing from reactive control to predictive and autonomous optimization, reducing variability, defects, and waste while maximizing productivity, resource efficiency, and sustainability. By leveraging data-driven decision-making, intelligent automation, and predictive maintenance, the framework enhances process reliability, prevents defects, and improves operational performance. Despite its advantages, LSS 4.0 presents challenges, including technological complexity, workforce upskilling, and organizational resistance. This study underscores the critical role of leadership-driven digital transformation, AI-augmented decision-making, and targeted skill development in fostering an innovation-driven manufacturing culture. Additionally, blockchain for secure supply chain traceability, augmented reality (AR) for enhanced humanmachine collaboration, and edge computing for decentralized intelligence are explored as key enablers of LSS 4.0âs full potential. Leadership commitment, cross-functional collaboration, and AI-driven Lean workflows are identified as essential success factors. Aligning digital transformation strategies with Lean principles and fostering a culture of continuous innovation is crucial for realizing LSS 4.0âs full benefits. Finally, this study highlights future research directions, emphasizing Industry 5.0 advancements such as human-centric automation, collaborative robotics, and sustainable smart manufacturingâkey drivers in building adaptive, intelligent, and resilient industrial ecosystems.
Aiming to boost production efficiency and reduce human workload, human-centricity has emerged as the core concept of Industry 5.0 (I5.0). However, current works have not established a unified automation and autonomous framework for human-centric smart manufacturing across various real world applications. Addressing this gap, this research introduces an innovative automated framework, ParallelWorkforce, which integrates blockchain intelligence and decentralized autonomous organizations and operations (DAOs) to drive the evolution from digital twins to parallel intelligence. First, this research conducts a comprehensive investigation into smart manufacturing in I5.0, summarizing the ongoing evolution. Next, a detailed exploration of ParallelWorkforce is provided to offer customized strategies for managing different levels of out-of-distribution events, significantly alleviating the workload on biological workers and maximizing the potential of both digital and robotic workers. Finally, the development of ParallelWorkforce across various key applications of smart manufacturing is demonstrated, including autonomous transportation, task assignment, and worker management. This research provides a viable solution for the further development of human-centered smart manufacturing and paves the way for the realization of â6Sâ goals in I5.0.
Larissa KrÀmer, Patrick Stuckmann-Blumenstein, Pascal Kaiser, Michael Henke · 6 authors
Enhancing transparency in production processes, especially in shared manufacturing, relies heavily on sharing data. Information asymmetries and coordination problems between parties with conflicting interests pose a challenge in this multi-stakeholder interaction. Blockchain technology with smart contracting can be a solution due to its immutable data and decentralised data storage features. Designing and executing blockchain in industrial applications is a highly intricate task that requires extensive testing, expertise, and proficiency. This paper is the first to propose a holistic simulation model for evaluating the impact of smart contracting on shared manufacturing, including a novel approach to simulated smart contracting in time-lapse for Ethereum-based networks. The introduced model guides the design and implementation process of blockchain applications in shared manufacturing to address this challenge. A systematic literature review establishes ten design process requirements and ten smart contract functions. The implementation is developed based on the design benchmarks of three Ethereum-based frameworks to investigate the simulation model's respective feasibility and scalability. The simulation model validation demonstrates our approach's suitability for simulating smart contracting in shared manufacturing within a hybrid production. It enables fast and scalable simulations, offering an innovative approach to extensively testing blockchain applications before their introduction to ongoing industrial operations.
Ardavan Babaei, Erfan Babaee Tırkolaee, Sadia Samar Ali
The utilisation of blockchain technology has gained significant traction within contemporary supply chains owing to its ability to enhance transparency, security, and traceability. Manufacturing plants, as pivotal components of the supply chain, stand to benefit from improved tracking and transparency of goods movement, real-time visibility, quality control processes, and adherence to industry standards through blockchain implementation. Nonetheless, without a comprehensive assessment of manufacturing plantsâ readiness to embrace blockchain technology, the anticipated benefits may give way to unforeseen challenges. In this study, a novel network framework is offered to evaluate manufacturing plantsâ readiness for adopting distributed ledger technology, specifically blockchain, under varying levels of ambiguity, including high (fuzzy) and low (scenario) ambiguity. This framework is distinguished by its ability to address uncertainty in evaluations, incorporating both scenario-based and fuzzy programming approaches. Furthermore, the framework treats evaluation criteria as interconnected entities, fostering a network perspective rather than a black-box approach. The proposed framework is then validated through a case study involving five manufacturing plants and twenty-four evaluation criteria. Our findings underscore the pivotal role of uncertainty considerations in ranking manufacturing plants, with the fifth plant emerging as the frontrunner across both fuzzy and scenario-based assessments in most instances.
Kaustabh Barman, Patrick Herbke, L Janssen, Elias Safo
In the dynamic realm of supply chain management, establishing trust and ensuring transparency are pivotal challenges, particularly in sectors like electric vehicle battery production. This paper introduces a Distributed Ledger Technology (DLT)-based framework utilizing chained Verifiable Credentials (VCs) to enhance traceability and reliability across the supply chain. By integrating smart contracts, our approach automates compliance and operational processes, facilitating real-time verification and fostering stakeholder trust. We present a scalable framework that answers three research questions about honest and accurate data entry by stakeholders, non-repudiation of asset data, and transactional event association with digital documents.
Raja Lavanya, G. Bhavani, C. Santhiya, G. Vinoth Chakkaravarthy · 5 authors
With the advent of the Internet of Things (IoT), a new transformation is predominant in the manufacturing industry, termed Industry 4.0. The revolution of IoT with artificial intelligence, Web3, robotics, and automation has transformed the traditional manufacturing system into a smart manufacturing system (SMS) by adding an intelligent component capable of automatic data collection through using sensors, processing data autonomously, and controlling machines remotely. However, adding automated intelligence, autonomous systems, and real-time data processing presents an insecure surface to cyber attackers to penetrate these cyber-physical systems (CPSs) and cause physical damage. This chapter presents a detailed discussion of cyber threats and incidents in the intelligent manufacturing industry, along with the available acceptable mitigation strategies. A taxonomy of cyber attacks on intelligent manufacturing systems clearly shows the difference between information technology threats and smart manufacturing cyber-threats. A detailed discussion on the limitations of SMSs in implementing cyber security is presented. Finally, some innovative machine learningâbased security mechanisms (ML-based intrusion detection systems) are discussed that promise to detect anomalies/intrusions in such systems.
This paper presents a novel approach for tracking products and processes within industrial multi-agent control systems by leveraging blockchain technology. The suggested solution facilitates the recording and validation of every step involved in creating a tailored product through the utilization of Ethereum-based smart contracts. The OWL ontology is used to describe agents and their capabilities and these software agents interact with IEC 61499 function blocks for process execution. The software agents record process events at each stage on the blockchain and the latter smart contract helps to trace and verify these process sequences of the customised product.
Do Hai Son, Nguyen Danh Hao, Tran Thi Thuy Quynh, Le Quang Minh
Decentralized applications (DApps) have gained prominence with the advent of blockchain technology, particularly Ethereum, providing trust, transparency, and traceability. However, challenges such as rising transaction costs and block confirmation delays hinder their widespread adoption. In this paper, we present our DApp named W2E - Workout to Earn, a mobile DApp incentivizing exercise through tokens and NFT awards. This application leverages the well-known ERC-20 and ERC-721 token standards of Ethereum. Additionally, we deploy W2E into various Ethereum-based networks, including Ethereum testnets, Layer 2 networks, and private networks, to survey gas efficiency and execution time. Our findings highlight the importance of network selection for DApp deployment, offering insights for developers and businesses seeking efficient blockchain solutions. This is because our experimental results are not only specific for W2E but also for other ERC-20 and ERC-721-based DApps.
In this chapter, we focus on offline operations of permissioned distributed ledgers (PDLs), which occur when nodes get disconnected from the main PDL. We will give an introduction to the offline mode and then discuss different offline scenarios. We then dwell on the various technical issues arising from the offline mode, followed by possible technical solutions. Finally, we reconcile all findings into an offline PDL architecture proposition.
The globalization of products and markets increases the distance between the origin of products and consumers. This leads to a condition where customers don't have information about the origins of their products. Thus, traceability has become an essential sub-system of manufacturing supply chain management. However, due to globalization and complexity of supply chain interactions among the suppliers and manufacturing enterprises, it is hard to pinpoint the exact contributions of different actors in a supply chain. Integrated supply network structure with suitable visibility and usage of real time data transfer is another area of great importance. This paper focuses on how NFT (Non-Fungible Token) coupled with smart contracts could utilize blockchain to make it easier to track the products in a supply chain. Explaining how NFT's could help in tracking the contributions of different stakeholders in a supply chain by tracking the product throughout the entire process of sourcing, production, and sale by using a digital twin. In a manufacturing supply chain enabled by NFT Technology, whenever raw materials are transferred and processed through the supply chain, an NFT would be attached to its digital twin which will capture the created values. Each NFT can easily and uniquely be known by its data stored. Data would be updated based on real time information and will enable the stakeholders to trace the product information about how much each company has contributed to the produced products. The data stored in the form of a smart contract in the blockchain prevents the data being entered from being destroyed, eliminated, or changed without permission. Thus, there is a secure data flow among different stakeholders.