Rommel Velastegui, RaĂşl Poler, Manuel DĂazâMadroĂąero
⢠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.
Innocent B. Ababio, Jan Bieniek, Mohamed Rahouti, Thaier Hayajneh ¡ 7 authors
Optimizing digital twins in the Industrial Internet of Things (IIoT) requires secure and adaptable AI models. The IIoT enables digital twins, virtual replicas of physical assets, to improve real-time decision-making, but challenges remain in trust, data security, and model accuracy. This paper presents a novel framework combining blockchain technology and federated learning (FL) to address these issues. By deploying AI models on edge devices and using FL, data privacy is maintained while enabling collaboration across industrial assets. Blockchain ensures secure data management and transparency, while explainable AI (XAI) enhances interpretability. The framework improves transparency, control, security, privacy, and scalability for self-optimizing digital twins in IIoT. A real-world evaluation demonstrates the frameworkâs effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations.
Amit Kumar Sharma, L. Ganesh Babu, Mrunalini Buradkar, M. Shanmathi ¡ 6 authors
PURPOSE: With a focus on enhancing transparency, lowering the risk of fraud, and ensuring ethical sourcing practices, this research aims to investigate how blockchain and IoT technologies can be incorporated into the diamond supply chain. This study addresses the complexities and challenges of implementing these technologies in an industry characterized by fragmented information sharing and centralized data storage. DESIGN/METHODOLOGY/APPROACH: Using both qualitative and quantitative analysis, the research uses a mixed-methods approach. While secondary data was obtained from previously published works, industry reports, and case studies, primary data was gathered through semi-structured interviews with professionals in the field. The implementation of the prototype system was carried out in three phases: Define, Operate, and Test. Ethereum was chosen for its smart contract capabilities, and various IoT sensors were deployed to monitor environmental conditions and track the real-time location of diamonds. FINDINGS: The integration of blockchain and IoT technologies significantly enhanced transparency within the diamond supply chain. The immutable nature of blockchain ensured tamper-proof records of transactions, while IoT sensors provided continuous real-time data, reinforcing transparency. The study observed a notable reduction in fraud due to the robust mechanisms of the system, which detected and prevented unauthorized alterations to the recorded data. Smart contracts automated compliance checks, ensuring adherence to ethical standards. Quantitative analysis revealed improvements in key metrics such as fraud reduction rates, transparency enhancements, and adherence to ethical sourcing standards. ORIGINALITY/VALUE: This study bridges a notable gap in existing research by focusing on the diamond supply chain. It provides comprehensive, data-driven insights and practical recommendations for industry stakeholders and policymakers. The results highlight how combining blockchain and IoT technology can improve operational efficiency, transparency, and ethical practices in the diamond business. It is also feasible and scalable. The study's methods and findings add a great deal to the body of information already in existence and provide a framework for further investigation and application in related situations.
⢠First comprehensive review of ML models for end-of-life product return predictions. ⢠Integration of PRISMA systematic review with network and meta -analysis methods. ⢠Key factors influencing ML prediction accuracy in reverse supply chains. ⢠Six future research directions for AI-enhanced circular-economy logistics. The evolution of the circular economy has led to the adoption of circular supply chains, where efficient management of the reverse supply chain enhances resource utilization, minimizes waste, and fosters a circular supply chain. However, managing reverse supply chains presents numerous challenges including a lack of information transparency and traceability, inconsistent cooperation among stakeholders, and uncertainty in recycling process, such as variations in quantity, quality, and timing. To address these challenges, an information sharing framework that integrates blockchain technology with digital product passports (DPPs) is designed to manage reverse supply chain information. Subsequently, a system dynamics model is applied to evaluate the potential impacts and feasibility of this framework within the reverse supply chain and its implications for the forward supply chain. The results indicate that the application of the proposed framework enhance the legal recycling market, reduces the negative environmental impact of illegal recycling activities, mitigates the bullwhip effect within the forward supply chain, and improves market fulfillment rate. The proposed information sharing framework can be employed to enhance the information efficiency of the reverse supply chain, aid in the recovery of end-of-life products and critical resources utilization, thereby supporting the transition to a circular economy.
This paper addresses critical food safety challenges in modern agricultural supply chain management by proposing an AI-driven quality chain system design. The system integrates five key chainsâagricultural product quality, capital flow, logistics, and accountabilityâinto a unified accounting information framework through artificial intelligence and multidimensional accounting theories, achieving "five-chain integration". Centered on "quality accountability", the intelligent open, decentralized, and industry-finance integrated agricultural supply chain management system enhances transparency, traceability, precision, and collaboration within the sector. It plays a vital role in establishing fair market competition, guiding industrial cycles, optimizing resource allocation, and building market confidence while reinforcing social responsibility.
J. Thimmia Raja, Ashish Ashish, Sindhu Boianapalli, Soma Sabitha M ¡ 6 authors
Blockchain technology, as a growing innovation, offers a viable solution to enhance transparency, traceability, and efficiency in global supply chains. While blockchain has great potential, several challenges remain, including scalability, integration with legacy systems, standardization, energy consumption, privacy concerns, and regulatory uncertainty. This enrollment of data is hoped to tackle these problems of dilemmas, whislt subsequent improved effective equates being more reasonable in terms of deploying blockchain applications. With the goal of offering a bridge to most projects still unsure whether to adopt Blockchain technology or to continue under what we call "Blockchain in the cloud" approach, this study introduces effective solutions on how to integrate Blockchain with current systems through innovative hybrid systems, standardized user protocols, and new consensus protocols that optimize cost of implementation and environmental impact. The other aspect of the research undertakes to design clear regulatory frameworks and change management systems that can aid in the adoption of blockchain technology for traditional supply chain stakeholders. By utilizing real-world case studies and the simplification of smart contract deployment, this work illustrates the practical benefits blockchain can provide in enhancing supply chain operations. In conclusion, the goal of this study is to pioneer a sustainable, secure, and efficient blockchain ecosystem that promotes trust, transparency, and collaboration among supply chain partners, ensuring the future viability of the technology.
Yahaya Saidu, Shuhaida Mohamed Shuhidan, Izzatdin Abdul Aziz, Md. Mahmudul Alam ¡ 7 authors
The integration of Blockchain (BC) and the Internet of Things (IoT) has emerged as a transformative solution for addressing traceability challenges in logistics, offering enhanced transparency, data security, and operational efficiency. This study presents a systematic review of the current state of BC-IoT integration for logistics traceability, focusing on its motivations, deployment strategies, technical implementations, and evaluation approaches. A total of 1,619 records were initially retrieved from IEEE Xplore, MDPI, ScienceDirect, Scopus, and Web of Science, from which 61 peer-reviewed studies published between 2015 and 2024 were selected and analyzed. The selection process adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous, transparent, and high-quality synthesis. Only studies explicitly addressing the convergence of BC and IoT within logistics traceability contexts were included. Key findings reveal that BC-IoT systems significantly enhance traceability and transparency across logistics networks but face persistent challenges such as scalability, latency, energy efficiency, and security. The review categorizes various deployment architectures including cloud, edge, fog, and hybrid models, and examines their implications for data responsiveness and system reliability. Additionally, it evaluates popular BC platforms (e.g., Hyperledger Fabric, Ethereum, Solana) and consensus mechanisms (e.g., RAFT, PBFT, PoS) based on their suitability for logistics applications. Emerging research directions emphasize the need for cross-chain interoperability, domain-specific frameworks, and decentralized traceability models, particularly in sectors such as humanitarian logistics and regulatory compliance. This review consolidates fragmented knowledge and provides actionable insights for developing scalable, secure, and transparent BC-IoT systems to support next-generation logistics operations.
Das Baumanagement steht aufgrund ineffizienter Zusammenarbeit, fragmentierter Abläufe und uneinheitlicher Dokumentation vor groĂen Herausforderungen. Diese Probleme behindern Vertragsverwaltung, Prozessverfolgung und Datenaustausch und fĂźhren oft zu Streitigkeiten, VerzĂśgerungen und KostenĂźberschreitungen. Blockchain-basierte Smart Contracts bieten Potenzial fĂźr Automatisierung, Transparenz und Sicherheit, werden jedoch bisher nur unzureichend genutzt und in BIM integriert, um branchenspezifische Anforderungen zu erfĂźllen. Diese Arbeit untersucht, wie Smart Contracts das digitale Baumanagement verbessern kĂśnnen - mit Fokus auf BIM-Datennutzung, automatischer Vorlagenerzeugung, ProzessĂźberwachung und Systemintegration. Die LĂśsung umfasst drei Verfahren: Datenmodellierung, Prozessvorlagendefinition und Smart-Contract-Implementierung. Validiert durch drei Anwendungsfälle und entwickelte Prototypen legt sie die Grundlage fĂźr ein zuverlässigeres und stärker kollaborativeres Baumanagement.
Industrial Cyber-Physical Systems (ICPSs) play a vital role in modern industries by providing an intellectual foundation for automated operations. With the increasing integration of information-driven processes, ensuring the ... | Find, read and cite all the research you need on Tech Science Press
Chiara Bartoli, Francesco Fasano, Francesco Cappa, Paolo Boccardelli
This paper examines the role that the metaverse and non-fungible tokens (NFTs) play in innovating the business model of firms. We employed qualitative data collected through a survey, which included more than 100 managers at both small and medium-sized enterprises and large enterprises representing over 20 different industrial sectors, and through a panel of experts. The empirical outcomes show that the metaverse and NFTs can be significantly leveraged by firms to innovate the building blocks of their business models in different ways, as summarized in the framework we have developed, which provides a guide to best practices when adopting these emerging technologies. In addition, this work also highlights the major challenges faced in the adoption of such technologies. In doing so, our aim is to advance overall scientific understanding of the metaverse and NFTs, which facilitates conscious implementation, and to highlight the potential for innovation that should be considered to favour business development.
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.
Cyber-Physical-Social Systems (CPSS), as emerging paradigms, are evolving to address the growing need for intelligent, adaptive, and transparent decision-making in complex environments such as smart cities and industrial systems. However, the advancements enabled by Digital Twins (DTs), centralized governance models, and opaque analytics limit scalability and resilience. In this study, we propose a decentralized governance framework that integrates Decentralized Autonomous Organizations (DAOs) and blockchain-based predictive analytics to enhance trust, interoperability, and ethical decision-making for CPSS. The proposed framework utilizes immutable ledgers, automated smart contracts, and community-driven governance to improve real-time collaboration, thereby ensuring system resilience and facilitating adaptive optimization. The remarkable innovation of the CPSS framework lies in combining digital tokens (DTs) within a blockchain application. We believe that our approach provides a scalable mechanism for autonomous decision-making and secure data sharing across multi-stakeholder ecosystems. In this regard, using theoretical analysis and comparative evaluations, we have demonstrated how our framework mitigates conventional challenges in CPSS governance, including security vulnerabilities, algorithmic fairness, and data integrity. Moreover, this research makes a significant contribution to the advancement of decentralized digital twin (DT) infrastructures, paving the way for more robust, ethically aligned, and resilient cyber-physical systems.
Abstract In the rapidly evolving landscape of IoT-enabled smart devices, significant challenges persist in integration to web3, security, and data reliability. This research presents the design and integration of IoT assets, particularly devices, through the Novel Decentralized Smart City of Things (DSCoT) framework. ESP32 microcontrollers serve as Ethereum clients, generating Externally Owned Accounts (EOA) for device identification and authentication. Despite resource constraints, including limited computational capabilities, essential libraries that manage tasks such as Wi-Fi module control, interaction with Ethereum-based blockchains, TCP connection management, and EEPROM operations for persistent data storage. The code is structured with functions for Wi-Fi setup, TCP API requests, and secure communication challenges. Integration involves compiling and flashing the code onto ESP32 devices, verifying EOA generation, and mapping devices, fog nodes, and users through smart contract interactions. The deployment process culminates in the generation of Non-Fungible Tokens (NFTs) for user authentication, with transaction verification on the Goerli testnet confirming successful DSCoT edge system implementation. This research underscores the importance of secure and decentralized integration of IoT-enabled smart devices to the blockchain, enhancing performance while ensuring security and transparency.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
This article presents a blockchain reconciliation framework that improves transparency, automation, and trust within the SAP supply chain and finance processes. The implemented system with smart contracts on SAP modules FI, MM, and SD permits real-time verification of supply chain activities and financial transactions, thus minimizing manual matching and third-party verification processes. The framework enables automated three-way matching and updates across modules by storing transaction states in a distributed ledger that captures changes. Based on experiments conducted using SAP simulation data, the accuracy of reconciliations increased by 92%, processing time was cut by 41%, and manual processing steps were reduced by 67%. The results demonstrate the capability of blockchain technology to solve pervasive challenges related data integrity and reconciliation within enterprise ERP systems.
The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse.
Abstract This research explores the contribution of Blockchain Technology and Industry 5.0 in driving sustainability within Bangladeshi Ready-Made Garments (RMG) industry, with a focus on alignment with key Sustainable Development Goals (SDGs). The study employs Interpretive Structural Modeling (ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods to identify and analyze 14 critical synergies that can drive sustainability. The ISM analysis categorizes the synergies into independent, dependent, and linkage variables, providing insights into their roles and significance within the system. Fuzzy DEMATEL further refines this understanding by evaluating the direct and indirect relationships among the linkage synergies. Key findings reveal the importance of synergies such as reverse logistics and recycling, supply chain collaboration & visibility and ethical practices in driving sustainability. This research contributes by offering a detailed analysis of how the synergy between Blockchain technology and Industry 5.0 can enhance sustainability practices in the RMG industry, providing actionable insights into the technological transformation of supply chain dynamics in support of global sustainability targets.
In all country construction projects include an enormous number of financial transactions and itâs necessary that the contracts between stakeholders should be more advanced in technology. The advance of Blockchain technology has been incontrovertible in recent years and this allowed the contracts to be programmed on Blockchain as smart contracts. The main goal of this paper is to identify and prioritize critical success factors of Smart Contracts in the construction industry. For identifying factors, we used library study and for prioritizing them, field study. To collect data, we designed a DANP questionnaire, from a panel senior managers and engineering experts. The results show that technology maturity is the most important factor in Smart Contract success; security of contracts, support from the engineering community, stakeholdersâ consideration, and competition in the building industry were other important factors which took their next levels.
Chalima Dimitra Nassar Kyriakidou, Iakovos Pittaras, Athanasia Maria Papathanasiou, George Xylomenos ¡ 5 authors
Despite its rapid growth, the Internet of Things (IoT) still faces significant challenges related to interoperability, transparency and security. To address these issues, we propose the utilization of smart contract-based Digital Twins (DTs) "hosted" in the Hyperledger Fabric blockchain network, while leveraging the Web of Things paradigm for interoperability. Thus, our solution includes several notable features, such as decentralization, auditability and security. However, implementing DTs using Distributed Ledger Technologies (DLTs) introduces certain overheads. In this paper, we assess the feasibility and evaluate the performance of smart contract-based DTs using a set of Key Performance Indicators (KPIs). Our results demonstrate that, although DLT-induced overheads, such as latency, are present, they remain manageable for IoT use cases.
Larissa Krämer, 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.
Mohamed Moetez Abdelhamid, Layth Sliman, Raoudha Ben Djemaa
Purpose: The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background: Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods: This research proposes an AI-enabled blockchain solution, ABISChain, designed to enhance the performance of supply chains. The system utilizes beliefs, desires, and intentions (BDI) agents to manage and prune blockchain data, thus optimizing the blockchainâs performance. A particle swarm optimization method is employed to determine the most efficient dataset for pruning across the network. Results: The AI-driven ABISChain platform demonstrates improved scalability, data consistency, and security, making it a viable solution for supply chain management. Conclusions: The findings provide valuable insights for supply chain managers and technology developers, offering a robust solution that combines AI and blockchain to overcome existing challenges in IoT-based supply chains.
K. S. Chandrasekaran, V. Mahalakshmi, M. R. Anantha Padmanaban
Over the past ten years, blockchain technology has significantly captured interest in various application fields. Originally devised for the Bitcoin peer-to-peer cryptocurrency network, extensive research now explores integrating blockchain with various other service domains. The technology is celebrated for its decentralized structure, robust security, immutability, and transparency. In blockchain systems, consensus algorithms play a crucial role in establishing unanimous agreement among participants within a distributed computing environment, facilitating the addition of new blocks to the blockchain network. The effectiveness and security of the network largely hinge on the performance of these consensus algorithms. However, existing consensus algorithms face challenges with throughput, latency, and communication complexity. To address these issues, an enhanced consensus algorithm known as Intuitive Random Selection based Byzantine Fault Tolerant (BFTIRS) is introduced. This algorithm optimizes the consensus process by selecting a subset of nodes, thereby reducing network complexity and enhancing efficiency without sacrificing security. To tackle scalability issues in blockchains, a hierarchical BFTIRS algorithm that incorporates sharding is developed. This approach segments network participants into local and global consensus groups, each conducting the consensus process independently. Performance evaluations of this algorithm show improvements in both efficiency and security over existing solutions.