Umar Yeni Suyanto, Ratna Rosita Pangestika, Kinanti Puja Prameswari, Heni Setiyaningsih
The integration of Artificial Intelligence (AI) into Small and Medium Enterprises (SMEs) has become a critical lever for achieving resilience, efficiency, and long-term sustainability in the digital era. However, despite AI’s transformative potential, empirical evidence suggests a persistent gap between technological capabilities and actual adoption within the SME sector. This study employs a bibliometric analysis using VOSviewer with the keywords "artificial intelligence" OR "AI" AND "Small and medium enterprises" OR "SMEs" AND "digital", encompassing 150 Scopus indexed articles from 2017 to 2025. The visualizations reveal six prominent thematic clusters, including AI based adaptive strategies, post-pandemic digital transformation, decentralized finance, digital literacy, and emerging concepts such as green cybersecurity. Notably, overlay visualizations indicate that sustainability-oriented digital practices are gaining scholarly momentum, signaling a future research trajectory focused on inclusive, secure, and environmentally conscious AI applications in SMEs. This article proposes a conceptual model SDRAIS (SME Digital Resilience through AI and Sustainability) that integrates three strategic dimensions: Strategic AI Integration, Digital Capabilities, and Sustainability Orientation. The model advances theoretical development by aligning with the Dynamic Capabilities and TOE (Technology Organization Environment) frameworks, while also responding to gaps in Triple Bottom Line (TBL)-driven technology adoption. The findings offer new perspectives for policymakers, SME stakeholders, and researchers by emphasizing the importance of interdisciplinary approaches to foster AI-driven innovation ecosystems that are both competitive and sustainable. This study contributes to the evolving discourse on digital transformation in SMEs and sets a robust foundation for future empirical exploration.
This research paper examines the structural and philosophical changes in global commerce and management required because of the "Third Wave" of digital transformation (DT). While earlier versions of DT dealt with the digitization of analog records and the uptake of cloud computing, the modern age of Agentic AI, Industrial Metaverse and Decentralized Autonomous Organizations (DAOs) is demanding a fundamental re-engineering of the firm. Through a methodical approach of analyzing current technological trajectories and management frameworks this study identifies the existence of a critical "Agility Gap" between legacy led organizations and the digital native enterprises. The research proposes Integrated Digital-Managerial Framework (IDMF) as strategic roadmap of 2026 and onwards. Key findings suggest that "Digital Maturity" is no longer a technology benchmark but comes instead as a cultural and structural imperative, one determining whether a company survives in the marketplace in an increasingly automated commerce landscape.
Abstract This chapter explores the integration of artificial general intelligence (AGI) and blockchain in circular manufacturing within Industry 5.0, emphasising sustainability and efficiency. AGI optimises resource use and waste reduction through advanced reasoning to improve data from internet of things (IoT) sensors and blockchain-based digital product passports. Blockchain ensures transparent, immutable tracking of material life cycles with smart contracts and tokenised models, enhancing automation and stakeholder trust. Despite challenges like cybersecurity, regulatory gaps and algorithmic bias, innovations such as zero-knowledge proofs and proof-of-stake consensus address these issues. The collaboration of AGI and blockchain drives human-centric systems, circular economy goals and sustainable manufacturing practices.
Shan Chang, Khuram Shahzad, Ali Nawaz Khan, Faisal Shahzad · 5 authors
Purpose This study aims to investigate the factors that drive and enhance sustainable supply chain management (SSCM) practices and performance, leveraging smart-contract-enabled green blockchain technology (BT). Specifically, it builds a research framework based on the dynamic capability view (DCV) theory to explore the interrelationships among green knowledge acquisition (GKA), generative AI (GAI), green BT, green organizational memory, and organizational environmental and operational performance. Design/methodology/approach A quantitative research approach was adopted. Cross-sectional survey data were collected via an online survey, yielding 318 valid responses from supply chain professionals in China. The hypothesized relationships within the proposed framework, grounded in DCV theory, were tested using Structural Equation Modeling via AMOS 24 and SPSS 23. Confirmatory factor analysis was conducted to validate the measurement model, and moderation effects were examined using mean-centered interaction terms within the SEM framework. Findings The analysis confirms the proposed framework’s validity and reveals several key findings: GKA positively impacts green BT. GAI significantly moderates the effect of GKA on green BT, strengthening the transformation of green knowledge into smart-contract-enabled blockchain capability. Green BT positively influences both environmental performance and operational performance. Green organizational memory acts as a crucial moderator, strengthening the positive relationships between green BT and both environmental and operational performance, amplifying the performance effects of green BT on both environmental and operational outcomes. Environmental performance ultimately contributes positively to operational performance. Practical implications Organizations in the supply chain sector can leverage these insights by prioritizing and maximizing GKA to effectively embed sustainability. Managers should promote the effective use of GAI in their processes, as it enhances the conversion of knowledge into tangible green BT implementations. Furthermore, maintaining a robust green organizational memory is recommended to maximize the performance returns from investments in green BT innovations. Originality/value This paper offers significant theoretical novelty by integrating GAI and green organizational memory as key dynamic capabilities within the DCV framework to examine green BT adoption in SSCM, a perspective previously underexplored. It is among the first to empirically confirm the moderating roles of both GAI and green organizational memory in the context of sustainable technology implementation, providing a unique model for achieving superior environmental and operational outcomes.
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 work presents a concept and implementation for the secure storage and transfer of quality-relevant data of milled workpieces from online-quality assurance processes enabled by real-time simulation models. It utilises Non-Fungible Tokens (NFT) to securely and interoperably store quality data in the form of an Asset Administration Shell (AAS) on a public Ethereum blockchain. Minted by a custom smart contract, the NFTs reference the metadata saved in the Interplanetary File System (IPFS), allowing new data from additional processing steps to be added in a flexible yet secure manner. The concept enables automated traceability throughout the value chain, minimising the need for time-consuming and costly repetitive manual quality checks.
Open access
3 source records
Digital Transformation in Industry
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Abstract Product life cycle management (PLM) in large supply chains still suffers from limited transparency, manual record-keeping, and weak traceability of provenance and expiry, which often results in counterfeit products, delayed recalls, and unsafe items reaching consumers. After the advancements in blockchain technologies, immutable, decentralised and auto-generated smart contracts provide a secure, safe, and organised solution for consent to the agreement between two or more parties and help digital assets and transactions to occur efficiently This work proposes a Blockchain–Internet of Things (B-IoT) based smart-contract framework that automates three key phases of the product life cycle: purchase order creation, invoice generation at delivery, and expiry-driven discard management. The proposed system will auto-trigger the smart contract through a program. It will generate the smart contract for a product when the purchase order is placed, generate an invoice, and handle the expired and discarded products. IoT devices will record important parameters such as product ID scanning, recording storage temperature, GPS trackers during transportation, etc. These parameters will help to auto-trigger the smart contract. To tune threshold parameters (e.g., temperature bounds, delay limits) and minimize cost–latency trade-offs, we build a lightweight regression model whose hyper-parameters are optimized using nature-inspired Mayfly (MFA) and Honey Badger (HBA) algorithms. The model predicts gas usage and latency per phase, achieving an RMSE of 0.15 and R 2 ≈ 0.9 on simulated transaction logs, while the final configuration yields an average execution efficiency of 92.3% and accuracy of 94.9% in correctly auto-triggering contract phases. The prototype is implemented using Ganache Truffle Suite, Remix, and MyEtherWallet, and evaluated in terms of gas consumption, functional correctness, and automation benefits over traditional manual contracts. Results demonstrate that the proposed B-IoT smart-contract framework provides transparent, tamper-resistant, and fine-grained product life management suitable for industrial deployments.
The Fourth Industrial Revolution, commonly referred to as Industry 4.0, represents a fundamental transformation of manufacturing systems through the integration of advanced digital technologies such as the Industrial Internet of Things (IIoT), artificial intelligence, big data analytics, cloud computing, and autonomous robotics. This paradigm shift enables the development of cyber-physical systems and smart factories characterized by real-time connectivity, decentralized decision-making, and data-driven optimization. The present study examines the conceptual foundations, technological pillars, and operational impacts of Industry 4.0, with particular emphasis on automation, productivity enhancement, and sustainability outcomes. Using a synthesis of recent empirical studies, global market data, and evidence from World Economic Forum “Lighthouse” factories, the paper evaluates how digital transformation influences manufacturing efficiency, energy use, emissions reduction, and workforce dynamics. The findings indicate that Industry 4.0 adoption significantly improves labor productivity, operational flexibility, and resource efficiency, while also presenting challenges related to cybersecurity, legacy system integration, and skills gaps. The study concludes that Industry 4.0 is not merely a technological upgrade but a strategic and organizational transformation essential for achieving competitive advantage and sustainable industrial development in an increasingly volatile global economy.
Amer A. Hijazi, Ali Alashwal, Milad Baghalzadeh Shishehgarkhaneh, Rodrigo N. Calheiros
. The integration of Blockchain with Building Information Modelling (BIM) addresses persistent challenges of transparency, accountability, interoperability, and trust in construction. Yet, systematic insights into Blockchain–BIM implementation remain limited. This study conducts a Systematic Literature Review (SLR), identifying six lifecycle stages where integration occurs, supported by 29 workflows and 30 technical methods. Applications include blockchain-secured design reviews, provenance tracking, automated procurement, smart payments, and immutable handover records. BIM data types—design metadata, cost and schedule data, IoT evidence, and compliance records—are mapped to smart contract functions and blockchain platforms. Key mechanisms such as hybrid on/off-chain storage, cryptographic hashing, access controls, and watermarking are comparatively analyzed. Results show permissioned platforms (e.g., Hyperledger Fabric) enable controlled collaboration, while public ones (e.g., Ethereum) support transparency and tokenization. The review provides a structured taxonomy and conceptual framework to advance theory and guide Blockchain–BIM adoption in complex construction projects.
Jay Daniel, Elias Abou Maroun, Jose Arturo Garza-Reyes, Asmae El Jaouhari · 5 authors
Purpose Blockchain serves as a vital technology for digital transformation within manufacturing supply chains through its improved security and transparent tracking capabilities. Blockchain technology emerges as a promising solution for supply chain stakeholders who face ongoing information asymmetries and trust deficits amidst growing demands for sustainable practices and ethical sourcing from consumers and regulators. This paper aims to explore the potential of blockchain technology to improve transparency within supply chain operations, particularly in the Australian electrical manufacturing industry. Design/methodology/approach This research uses a multimethod research design encompassing three phases: Phase 1, a comprehensive literature review; Phase 2, semistructured interviews; and Phase 3, a case study to explore the application of distributed ledger technology for secure real-time supply chain activity monitoring. Findings The results suggest that integrating blockchain with Internet of Things technologies and sensor-based data leads to substantial improvements in data integrity while reducing fraud risks and enabling more efficient supply chain actor collaboration. Practical implications Manufacturing firms can benefit from our research findings, which provide actionable steps for using blockchain to achieve sustainable operations and improved efficiency. The authors offer strategic guidance for firms to develop supply chains that ensure transparency and resilience, along with alignment to environmental, social and governance goals. Originality/value The study advances digital supply chain transformation research by showing how blockchain serves as an essential technology to improve transparency and trust while boosting performance in manufacturing supply chains.
With the advancement of sensing technology, the use of spatial information from LiDAR and similar measurement devices such as a depth camera is rapidly expanding. However, 3D spatial data contains trade secrets such as facility layouts and equipment configurations, making direct sharing a significant business risk. Additionally, from the perspective of data distribution between companies, a mechanism to prove the value of data utilization before purchase is essential. Existing approaches using trusted third parties or conventional encryption require data disclosure for utility verification, failing to achieve both confidentiality and value assessment simultaneously. Therefore, this study proposes a distributed platform that enables secure data exchange between organizations while ensuring confidentiality of 3D spatial information using cryptographic methods. The system operates on a Hyperledger Fabric-based permissioned blockchain to establish trust through immutable proof verification records for data distribution, and enables verification of data utility without disclosing any original data through zero-knowledge proof technology. Specifically, we implement a proprietary algorithm that generates feature values with concealed coordinates while preserving the geometric characteristics of the spatial information. Each participating organization generates feature values from spatial information and records proofs of the validity of this process on the blockchain, allowing other organizations not only to search for useful spatial information based on the feature values but also to verify the reliability of the feature values themselves. This enables previously difficult applications such as collaborative digital twin construction with competitors in manufacturing and logistics industries. Through empirical experiments, we clarify practical processing speeds in a consortium of multiple organizations, confirming the applicability in enterprise environments.
Martin Brennecke, Simon Mertel, Tobias Guggenberger, Johannes Sedlmeir · 6 authors
Zusammenfassung Auf dem Weg zu einer kreislauffähigen Wertschöpfung nimmt die lückenlose Dokumentation von Produktionsketten eine elementare Rolle ein: Sie erlaubt es, eingesetzte Ressourcen und Schritte im Wertschöpfungsprozess nachzuvollziehen und nachhaltigkeitsbezogene Angaben überprüfbar und somit vermarktbar zu machen. In diesem Kontext wird immer wieder über die Blockchain-Technologie diskutiert. Neben den Chancen, die eine Blockchain für eine verifizierbare Dokumentation und Interaktionen über Organisationsgrenzen hinweg bietet, werden in diesem Beitrag die Herausforderungen ihrer Nutzung aufgezeigt. Dabei wird auch auf komplementäre Technologien, insbesondere kryptographische Ansätze für digitales Identitätsmanagement und Zero-Knowledge Proofs, eingegangen und gezeigt, wie diese zur Bewältigung der Herausforderungen genutzt werden können.
In the domain of agricultural product traceability, while traditional blockchain technologies ensure data immutability, they struggle to verify the authenticity of digital twins generated by generative artificial intelligence (GAI), resulting in a se mantic gap between the physical world and its virtual representation. To address these challenges, this paper proposes Verifiable Twin model driven by Cross-Modal Alignment (VTA-CMAD), which targets three core issues: cross-modal consistency verification between blockchain-stored data and AIGC-generated twins, lightweight zero-knowledge proof framework construction, and incentive-compatible suppression of malicious behaviors. The innovation of this article is reflected in three aspects. Firstly, this article proposes a 3D multimodal alignment algorithm that integrates dynamic time warping. B y integrating physical sensing temporal data, production process images, and cultural semantic descriptions, the optimal transmission mapping of feature space is established. Secondly, design a verifiable circuit zk Vector to transform the inference process of the fine-tuning diffusion model into zero knowledge proof constraints, generating proof files with a size less than 1.2KB. Finally, a dynamic consensus mechanism Proof of Trustworthiness based on Feature Alignment (PoTV) based on feature alignment is constructed to achieve adaptive adjustment of data weights. Experimental results demonstrate that the proposed approach achieves a tamper detection rate of 86.2%, a Gini coefficient of 0.19 for incentive fairness, and reduces multimodal alignment error to 0.11 ± 0.03.
Traditional electronic Kanban (eKanban) systems depend on manual scans and offer only discrete material visibility, limiting responsiveness and automation in lean manufacturing environments. These operational bottlenecks are magnified in high-mix contexts, where delayed replenishment signals degrade flow stability, increase work-in-progress, and hinder sustainable material handling. Furthermore, vendor-specific systems lack interoperability for scalable automation, constraining the development of intelligent manufacturing solutions. This work investigates whether zone-based replenishment automation can be enabled through real-time locating systems (RTLS) using open interoperability standards, addressing a gap in empirical validation of such approaches. A middleware architecture was developed that integrates ultra-wideband (UWB) positioning, an Omlox-compliant location middleware (DeepHub), and a cloud-based eKanban system to replace manual triggers with geofence-driven order creation. The novelty of this study lies in demonstrating a fully automated Kanban signaling loop built on the open Omlox standard, providing vendor-independent RTLS interoperability and eliminating human intervention in replenishment signaling. This contributes new knowledge on how continuous location data can be converted into actionable replenishment events in a standards-based, modular manner, enabling more intelligent and autonomous material-flow control. A controlled proof-of-concept experiment simulating shop-floor conditions showed that the system achieved a 100% detection success rate, zero duplicate orders, and an average trigger-to-action latency of 2.7 s, while automatically recovering from authentication and WebSocket failures. These results provide the first empirical evidence that Omlox-compliant RTLS middleware can reliably support zone-based eKanban automation. The findings have direct implications for intelligent and sustainable manufacturing by demonstrating a scalable pathway toward interoperable, real-time material-flow systems that reduce manual intervention, avoid unnecessary handling, and lower work-in-progress. More broadly, the work addresses the current lack of empirical validation of open-standard RTLS integration within lean and sustainable production environments.
Barbara Bigliardi, Virginia Dolci, Alberto Petroni, Benedetta Pini
How are digital technologies transforming public sector supply chains, and what factors condition their effectiveness? Despite the growing interest in this domain, the literature remains fragmented, with a lack of longitudinal studies, citizen-centered evaluations, and cross-country comparisons. This study addresses these gaps through a systematic review of 71 Scopus-indexed articles, combining descriptive mapping with a keyword-based bibliometric analysis. The approach identifies consolidated and emerging themes, particularly within the “Business, Management and Accounting” subject area, where methodological heterogeneity and limited generalizability persist. Findings reveal increasing scholarly attention to technologies such as blockchain, AI, and e-procurement, highlighting both operational modernization and newer concerns such as sustainability, digital governance, and decentralized finance. The paper contributes by structuring dispersed knowledge into a coherent framework, offering a roadmap for research and practical guidance for public administrators seeking value-driven digital transformation.
The swift expansion of IoT devices in smart cities demands decentralized and open systems of attentive exchange of assets in automotive supply chains. Nevertheless, the majority of the available blockchain-based solutions are focused on traceability and ignore scalability, conditional payment automation, and real-time IoT verification. To overcome those pitfalls, the research proposes a Blockchain-based framework implemented on Hyperledger Fabric, incorporating Non-Fungible Tokens, and escrow-based smart contracts, to facilitate verifiable, automated vehicle transactions. The payment is conditionally released, and the vehicle is represented as a discrete NFT that undergoes authenticated release under Fabric Certificate Authority with escrow verification. Sub-millisecond latency (0.0003 s), constant throughput, and minimal computational cost experimentally verify the effectiveness of the framework in terms of its efficiency, privacy, and scalability in the efficient and autonomous exchange of assets in next-generation smart cities.
The EU Deforestation Regulation (EUDR), Regulation (EU) 2023/1115, requires operators and traders placing cattle, cocoa, coffee, palm oil, soya, wood, rubber, charcoal and their derived products on the EU market to demonstrate that the underlying commodities are deforestation-free after 31 December 2020, are legally produced, and are covered by a due diligence statement. While deforestation detection is often treated as the technically dominant requirement, EUDR compliance is broader: it also requires traceability across supply chains, provenance of production, country-specific legal context, and auditable due diligence processes. Translating these obligations into an inspectable, reproducible, software-supported workflow raises three coupled problems: (i) the regulation itself is a moving artefact whose definitions, country risk classifications and implementing acts evolve; (ii) the geospatial evidence used to satisfy Article 3(a) depends on upstream datasets-primarily the Hansen Global Forest Change product-whose versions, tile schemes and methodological conventions also change; and (iii) the resulting compliance interpretations cannot be ethically delegated to a fully autonomous agent because they affect market access, livelihoods and the legal exposure of operators. This paper describes an architecture that addresses these problems jointly through a closed feedback loop linking regulation, data dependencies, implementation, validation, and governance. We separate authoritative deterministic generation of evidence from a public, non-authoritative Digital Twin portal that exposes system state, dependencies, and example outputs for inspection. A procedural Decentralized Autonomous Organization (DAO)implemented in this work as a file-grounded YAML proposal workflow, but compatible with optional blockchain anchoring of evidence digests and proposal records-closes the governance loop between stakeholders, developers, and evolving regulatory interpretation. The procedural design is deliberate: as we discuss below, the kind of DAO appropriate for governing truth claims about the physical world differs in object, voting subject and failure mode from the protocol-governance DAOs commonly associated with the term, and the choice to run the governance layer off-chain reflects that difference rather than a rejection of distributed-ledger technology as such. An LLM-based Digital Twin Engineer (DTE) agent supports inspection and proposal drafting under strict grounding rules, but never executes code or makes compliance determinations. We describe the multi-repository implementation, the deterministic evidence bundle contract, the public/private trust-zone separation that protects per-operator plot data while allowing example reports to be inspected publicly, and the regulation-as-dependency feedback loop that forces reruns of impacted methods when upstream artefacts change. Although the current implementation focuses primarily on geospatial deforestation evidence, the proposed pipeline is intended as a practical starting point for progressive enrichment as new forms of land intelligence, supply-chain transparency, legal provenance data, and business-network evidence become available. We argue that this design is a generalisable pattern for compliance domains in which regulatory requirements evolve over time and implementations must remain inspectable, reproducible, extensible, and corrigible by humans.