Journal of Industrial Information Integration最新文献

{"title":"Assessment model for Industry 5.0: A holistic approach to readiness and integration","authors":"Cesar Cuevas-Lopez-de-Baro,&nbsp;Ignacio Mira-Solves,&nbsp;Antonio Verdú-Jover","doi":"10.1016/j.jii.2025.100855","DOIUrl":"10.1016/j.jii.2025.100855","url":null,"abstract":"<div><h3>Purpose</h3><div>This study proposes a novel assessment model tailored to the unique requirements of Industry 5.0 transformation utilizing Socio-Technical Theory as a framework. The model seeks to give organizations actionable insights into navigating the complex socio-technical dynamics of I5.0, evaluating organizational readiness holistically, and guiding their transition to this new industrial paradigm across different perspectives and dimensions. By fostering this holistic approach to Industry 5.0 adoption, our study aims to impact industry, society, and institutions significantly.</div></div><div><h3>Methodology</h3><div>The model was developed using a two-phase research approach. The first phase involved a detailed systematic literature review and a systematic literature network analysis to identify the perspectives, critical dimensions, and guiding questions for the assessment based on Socio-Technical Theory. The second phase refined the preliminary model through expert feedback from industry specialists.</div></div><div><h3>Findings</h3><div>Our research proposed an assessment model for Industry 5.0, confirmed Socio-Technical Theory as a substantive framework, and identified four core perspectives: strategy, sustainability, human-centricity, and resilience. These perspectives encompass 25 critical dimensions, which were further analyzed through 64 guiding questions. Expert feedback validated the need for the model and highlighted its potential for application from both a strategic and a tactical perspective.</div></div><div><h3>Originality</h3><div>This study addresses a crucial gap in the literature by presenting a novel assessment model tailored to the unique challenges and opportunities presented by this emerging industrial paradigm. The model’s emphasis on holistic readiness and the integration of business strategies goes beyond conventional approaches by fostering a more nuanced understanding of the socio-technical dynamics shaping the future of industry and society. By applying Socio-Technical Theory (STT), this study provides a more comprehensive understanding of the socio-technical dynamics underlying I5.0 transformation. This innovative approach provides organizations with actionable insights into navigating the intricate interplay between advanced technologies, human factors, sustainability principles, and resilience strategies in complex situations, thus advancing both theoretical and practical knowledge in the field of Industry 5.0.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100855"},"PeriodicalIF":10.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid deep learning based threat intelligence framework for Industrial IoT systems","authors":"Jahanzaib Malik ,&nbsp;Adnan Akhunzada ,&nbsp;Ahmad Sami Al-Shamayleh ,&nbsp;Sherali Zeadally ,&nbsp;Ahmad Almogren","doi":"10.1016/j.jii.2025.100846","DOIUrl":"10.1016/j.jii.2025.100846","url":null,"abstract":"<div><div>The exponential growth of Industrial Internet of Things (IIoT) is a major driving force behind Industry 4.0. Besides complete automation and transformation, industrial IoT has so far created plenty of opportunities in several sectors 1.3such as smart manufacturing, energy, healthcare, smart agriculture, retail, supply chain, and transportation. However, the increased pervasiveness, reduced human involvement, resource-constrained nature of underlying IoT devices, dynamic and shared spectrum of 4G/5G communication, and reliance on the cloud for outsourced massive storage and computation bring novel security challenges and concerns. A significant challenge currently confronting the Industrial Internet of Things (IIoT) is the increasing prevalence of sophisticated IoT malware threats and attacks. To address this, the authors propose a hybrid threat intelligence framework that is not only highly scalable but also incorporates self-optimizing capabilities, enabling it to counteract a wide range of persistent cyber threats and attacks targeting IIoT systems. For a comprehensive evaluation, the authors utilized the state-of-the-art TON_IIoT dataset, which includes over 3 million instances representing various adversarial patterns and threat vectors. In addition, both standard and extended performance evaluation metrics were employed to ensure a thorough assessment. The proposed approach was also compared against several contemporary deep learning-based architectures and existing benchmark algorithms. The results indicate that the proposed method achieves superior detection accuracy, with only a minimal compromise in speed efficiency. Finally, a 10-fold cross-validation was conducted to provide an unbiased evaluation of the framework’s performance.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100846"},"PeriodicalIF":10.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-agent collaboration mechanisms based on distributed online meta-learning for mass personalization","authors":"Ziren Luo ,&nbsp;Di Li ,&nbsp;Jiafu Wan ,&nbsp;Shiyong Wang ,&nbsp;Ge Wang ,&nbsp;Minghao Cheng ,&nbsp;Ting Li","doi":"10.1016/j.jii.2025.100852","DOIUrl":"10.1016/j.jii.2025.100852","url":null,"abstract":"<div><div>Driven by the mass personalization model, online meta-learning has garnered significant attention from resource-constrained agents due to its wide adaptability, continuous learning, and lightweight characteristics. However, as cutting-edge artificial intelligence advances, the intelligence and autonomy of agents are increasingly improving, posing challenges to data synchronization and decision-making consistency in collaborative processes. To this end, this paper proposes a distributed online meta-learning multi-agent collaboration framework based on hybrid parallelism, which meets the needs of synchronous collaboration and asynchronous collaboration in different stages of personalization. To implement this framework, we designed two key algorithms. First, an agent clustering algorithm based on graph theory groups similar agents. Synchronous collaboration within the group satisfies the manufacturing time constraint, while asynchronous collaboration among groups ensures decision consistency. Second, a multi-agent online meta-learning algorithm with gradient tracking monitors global gradients through limited communications, accelerating rapid adaptation to personalization tasks. Finally, we validated our approach through experimental testing on a personalized production platform. The results underscore the effectiveness of the proposed multi-agent collaboration mechanism and implementation algorithms, providing a new solution for multi-agent collaboration based on artificial intelligence in mass personalization environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100852"},"PeriodicalIF":10.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated IoT-based production, deep learning, and Business Intelligence approaches for organic food production","authors":"Nicola Contuzzi ,&nbsp;Angelo Maurizio Galiano ,&nbsp;Giuseppe Casalino","doi":"10.1016/j.jii.2025.100850","DOIUrl":"10.1016/j.jii.2025.100850","url":null,"abstract":"<div><div>The organic food processing industry grapples with several complex challenges, such as ensuring the ingredients' authenticity, reducing resource consumption, and maintaining consistent product quality despite fluctuating demand and the supply seasonal nature. Previous methodologies often lacked integration of real-time data and advanced predictive analytics, leading to inefficiencies and increased waste. This study proposes a novel framework that combines IoT sensor networks, deep learning algorithms, and business intelligence to optimize production processes in organic tomato processing. By employing a Long Short-Term Memory (LSTM) model, the framework effectively predicts sales, manages raw material procurement and enhances logistics based on real-time data inputs. Findings indicate a 25 % improvement in productivity and a 20 % reduction in waste during production, alongside a 30 % increase in profitability attributed to informed pricing strategies and enhanced supplier quality management. The integration of predictive analytics not only aligns production with consumer demand but also supports sustainable practices by minimizing overproduction and waste. This work addresses the critical intersection of technology and sustainability in food production, ultimately contributing to a more resilient and efficient organic food supply chain. Keywords: Organic food, data mining, deep learning, Business Intelligence</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100850"},"PeriodicalIF":10.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twin-enabled regional food supply chain: A review and research agenda","authors":"José Monteiro,&nbsp;João Barata","doi":"10.1016/j.jii.2025.100851","DOIUrl":"10.1016/j.jii.2025.100851","url":null,"abstract":"<div><div>Sustainable, resilient, and efficient Regional Food Supply Chains (RFSCs) are critical to addressing global challenges such as food security, climate change, and resource optimization. Digital Twins (DTs) have emerged as powerful tools for real-time monitoring, predictive analysis, and process optimization, providing actionable insights for the modernization and integration of RFSCs. Our systematic literature review of 133 studies reveals how DTs can transform RFSCs and the challenges and opportunities associated with their adoption. The findings suggest that DTs enhance RFSC management by facilitating informed decision-making, improving resource efficiency, reducing waste, and promoting regional resilience to external disruptions. This work advances the state of the art by identifying the unique role of DTs in optimizing each process within RFSCs, from production to consumption. Key contributions include (1) identifying the potential of DTs to improve sustainability, resilience, and efficiency in RFSCs, (2) analyzing the challenges of DT interoperability, data integration, and cybersecurity, (3) exploring how DTs can foster regional development through improved traceability and logistics, and (4) presenting an annotated research agenda. This review offers a comprehensive theoretical framework and practical guidance for researchers and practitioners leveraging DTs in RFSCs to create sustainable, human-centric, and resilient supply chains at a regional scale.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100851"},"PeriodicalIF":10.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed Koopman model predictive control of open canal systems","authors":"Ningjun Zeng ,&nbsp;Lihui Cen ,&nbsp;Wentao Hou ,&nbsp;Yongfang Xie ,&nbsp;Xiaofang Chen","doi":"10.1016/j.jii.2025.100845","DOIUrl":"10.1016/j.jii.2025.100845","url":null,"abstract":"<div><div>The physical model of open canal systems is described by the Saint-Venant (S-V) equations, which are partial differential equations without explicit solutions. Consequently, the control problem of open canal systems is not trivial. In this paper, a model predictive control (MPC) method based on the framework of the Koopman operator and the physics-informed neural networks is proposed. A continuous-time Koopman model is obtained by mapping the system states, including water levels and discharges, from the original state space to a raised-dimensional observation space. An autoencoder architecture is developed to approximate the mapping to the raised-dimensional space. Specifically, we established a numerical connection between the Koopman model and the S-V equations, and introduced a physics-informed loss function. A two-stage training strategy is implemented to obtain the optimal approximation of the physics-informed Koopman model. Subsequently, a continuous-time stable MPC method for the physics-informed Koopman model of open canal systems is proposed via control parameterization. The proposed method was validated on a one-reach canal system and a cascaded system. The simulation results demonstrate that the physics-informed Koopman model accurately predicts the future dynamics of open canal systems, and the MPC controller effectively tracks the desired water levels.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100845"},"PeriodicalIF":10.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative decision-making modelling for risk analysis in industrial informatization of infrastructure project","authors":"Song-Shun Lin ,&nbsp;Xin-Jiang Zheng ,&nbsp;Muhammet Deveci","doi":"10.1016/j.jii.2025.100849","DOIUrl":"10.1016/j.jii.2025.100849","url":null,"abstract":"<div><div>Rapid economic growth has driven the increasing scale and complexity of infrastructure projects, introducing significant challenges associated with uncertainty and risk. Approaches relying primarily on engineering judgment lack the capacity to effectively capture and quantify these uncertainties in complex project environments. This study introduces a novel multi-criteria decision-making approach utilizing spherical fuzzy sets to enhance the informatization and integration of risk management processes in industrial contexts. The proposed approach integrates multi-source evaluations, enabling the accurate calculation of criteria weights and fostering robust information processing capabilities. Through sensitivity and comparative analyzes, the developed approach demonstrates its effectiveness in managing multi-source risk assessments, facilitating informed decision-making. A decision clarity index is introduced to quantitatively assess the impact of varying decision-making conditions on risk source identification. This study advances industrial information integration by integrating mathematical models with risk management practices, offering a structured approach and practical strategies to enhance decision-support systems for infrastructure projects in complex industrial environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100849"},"PeriodicalIF":10.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twin-driven intelligent spinning technique for curved surface parts","authors":"Pengfei Gao,&nbsp;Xinshun Li,&nbsp;Xinggang Yan,&nbsp;Hongwei Li,&nbsp;Mei Zhan","doi":"10.1016/j.jii.2025.100848","DOIUrl":"10.1016/j.jii.2025.100848","url":null,"abstract":"<div><div>Spinning is an advanced forming technology widely used in manufacturing of curved surface parts in petrochemical, aviation and aerospace industries. Since the spinning is a local loading and incremental forming process, the workpiece forming status and forming rules are both complex and time-varying, which pose great challenges to the precisely control of spinning process. To address this, a novel digital twin-driven (DT-driven) intelligent spinning technique was proposed. It develops a non-contact measuring device to monitor the workpiece forming status. Utilizing both real-time and historical monitoring data, a twin model of forming status evolution is constructed using deep neural networks. In addition, an efficient multi-objective optimization method is established to achieve online dynamic optimization of spinning process. By integrating the above technologies, the developed DT-driven intelligent spinning technique can well capture the real-time workpiece forming status and time-varying forming rules, moreover, intelligently and gradually design the optimal process aligned with the time-varying forming rules throughout the spinning process. This changes the traditional trail-and-error spinning method, which predetermines the entire process by characterizing it as a linear time-invariant process, thus effectively enhancing forming quality, forming efficiency, and environmental sustainability.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100848"},"PeriodicalIF":10.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A blockchain-enabled information as a service (IaaS) system for crowdsourced manufacturing: A crowdsourcing case study of tank trailer manufacturing","authors":"Mulang Song,&nbsp;Xuejian Gong,&nbsp;Roger J. Jiao,&nbsp;Roxanne Moore","doi":"10.1016/j.jii.2025.100844","DOIUrl":"10.1016/j.jii.2025.100844","url":null,"abstract":"<div><div>Crowdsourced manufacturing leverages extensive collaboration among the cyber platform, innovators, and service providers to configure product fulfillment throughout the supply chain. Information as a Service (IaaS) emerges as a crucial and promising competency for crowdsourced manufacturing. Nevertheless, implementing autonomy, security, and decentralization for IaaS fulfillment in a crowdsourcing environment poses challenges. This paper proposes a blockchain-enabled solution for an IaaS fulfillment system to execute crowdsourced tasks and manage interactions and information flows across a cyber platform for crowdsourcing. Through critical use case analysis, we examine the workflows of crowdsourced manufacturing and the associated information flows. An IaaS fulfillment system is suggested to provide information management services using blockchain technology. This proposed IaaS system encompasses a distributed blockchain network that facilitates secured information upload and management services for information sharing. The IaaS system employs a web-based interface, smart contracts, and IPFS algorithms over a blockchain network to offer IaaS to users, allowing them to conveniently and securely upload and retrieve product fulfillment statuses at low trust costs. A case study of tank trailer crowdsourced manufacturing is provided to validate the feasibility and potential of the proposed blockchain-enabled IaaS system.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100844"},"PeriodicalIF":10.4,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STEP-based Model Recommendation Method for the Exchange and Reuse of Digital Twins","authors":"Chengfeng Jian,&nbsp;Zhuoran Dai,&nbsp;Junyu Chen,&nbsp;Meiyu Zhang","doi":"10.1016/j.jii.2025.100839","DOIUrl":"10.1016/j.jii.2025.100839","url":null,"abstract":"<div><div>To support the design and optimization of human-centric manufacturing systems in the Industry 5.0 era, Model Based Definition (MBD) models with STEP knowledge graph (STEP KG) recommendation are crucial for exchanging and reusing digital twin models. Existing methods based on graph convolutional networks (GCN) focus on geometric semantics but overlook the needed correlation engineering semantics in the STEP KG. Our paper introduces a Quaternion Diffusion Graph Convolutional Network (QDGCN) recommendation framework, comprising quaternion semantic diffusion and quaternion parameter diffusion. The quaternion semantic diffusion method uses quaternion to combine multiple layers of semantic diffusion into a single set transformation operation and constructs the quaternion-based multi-layer semantic model on the STEP KG. The quaternion parameter diffusion method uses a quaternion parameter generation mechanism based on the diffusion model. It generates different weight coefficients for identifying the main node features in the STEP KG. The fusion of the two solves the inconsistency problem between geometric and engineering semantics. We compared QDGCN with state-of-the-art methods on real datasets, and the detailed analysis of experimental results demonstrates the effectiveness of QDGCN.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100839"},"PeriodicalIF":10.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}