This paper reviews the literature to analyse the use of artificial intelligence (AI) in collaborative processes among supply chain (SC) partners, thereby forming a collaborative network (CN). Given the growth of AI and its limited exploration in many business strategies, especially when collaboration among SC partners’ is established, this paper focuses on defining the lines of research and application

Detecting bolt loosening in bolted structures under non-Gaussian noisy environments is challenging with existing time-frequency analysis methods. This study proposes a novel fractional lower-order fractional synchrosqueezing-extracting transform (FLOFSSET) to effectively process linear chirp signals in such environments. Additionally, a data fusion approach based on the Dezert-Smarandache Theory is

The increasing complexity and high aspect ratios of next-generation semiconductor structures have intensified pattern loading effects in atomic layer deposition (ALD) processes. These effects result in non-uniform thin-film deposition rates and thickness variations. Deriving optimal process conditions to ensure consistent thin-film deposition is essential for maintaining substrate uniformity and enhancing

The development of privacy-preserving data exchange protocols through Privacy Set Intersection (PSI) protocols has emerged as a critical enabler for secure information exchange in the Internet of Medical Things (IoMT), particularly for applications requiring coordinated data analysis across distributed healthcare systems. Current PSI implementations face two fundamental limitations: a lack of efficient

Unmanned aerial vehicles (UAVs) and autonomous ground vehicles are increasingly outfitted with advanced sensors such as LiDAR, cameras, and GPS, enabling real-time object detection, tracking, localization, and navigation. These platforms generate high-volume sensory data, such as video streams and point clouds, that require efficient processing to support timely and informed decision-making. Although

Directed Energy Deposition (DED) has gained significant interest from the industrial sectors due to its ability to fabricate medium-to-large scale parts with high productivity and low capital investment. Within DED technologies, DED-Arc stands out as a promising method for practical industrial applications. However, the process planning presents challenges for developing an automated system suitable

In response to complex retail challenges, the elasticity of production and logistic efficacy can minimize expenses, which always meets consumer satisfaction. The approach evolves by implementing a retail logistics strategy to integrate advanced production information under diverse trading modes. The research promotes a high level of consumer service, and on the other hand, the remanufacturing establishes

The advent of virtual simulation and Augmented Reality has had a profound impact on industrial processes, enhancing operational efficiency and optimising production workflows. This paper presents a comprehensive study on the development and implementation of a Mixed Reality Lab designed for the assembly and disassembly of industrial products. The primary objective is to create an advanced and intuitive

This article examines the strategic integration of artificial intelligence (AI) in human resource management (HRM), highlighting both its opportunities and its challenges. While AI can improve HRM functions such as recruitment, performance evaluation and employee development, it also raises concerns related to algorithmic bias, technostress and resistance to change. To navigate these complexities,

The efficient measurement and optimization of the particle size distribution (PSD) of recycled coarse aggregates (RCA) is critical to ensuring consistent quality in high-performance concrete production. Unlike primary aggregates, which typically demonstrate minimal variability over extended periods and require only occasional testing, RCA often exhibit substantial fluctuations in quality over short

This study addresses a critical gap in the literature by providing a comprehensive analysis of the organizational opportunities and challenges linked to increasing digitalization within the context of New Product Introduction (NPI), with a particular focus on large organizations within the manufacturing industry. The study introduces a framework that integrates opportunities, challenges, and tentative

Fault diagnosis is a critical component of prognostics and health management, enhancing machinery reliability and ensuring operational efficiency by enabling proactive maintenance strategies. However, achieving this requires high data fidelity to accurately predict the full spectrum of faults and structural degradation for reliable assessments. AI-driven fault diagnostics based on machine learning

High-fidelity digital twin modeling is the core of digital twin machine tool (DTMT) to achieve accurate mapping and deliver functional services. Model fusion is a key modeling technology to promote the integrity and system connectivity of DTMT. However, current model fusion lacks attention to the multi-scenario characteristics of DTMT, which hinders the effective application of DTMT. Therefore, this

Identification of microstructures is the core of materials engineering. Artificial intelligence's application in materials engineering has recently shown the possibility of realizing complicated tasks. Identifying elemental distribution in microstructure requires experimentation or computationally intensive modeling techniques. The current work focuses on the question, can artificial intelligence predict

This study explored the applicability of Industry 4.0 (I4.0) technologies in oil and gas (O&G) pipeline leakage monitoring (PLM) in Tanzania. Specific objectives identified factors affecting the adoption of I4.0 technologies in the O&G PLM, evaluated the maturity of I4.0 within the industry, and proposed strategies to enhance the adoption of I4.0 technologies for PLM. A mixed-methods design gathered

This study focuses on resilient supplier selection and order allocation — two crucial aspects of modern supply chain management (SCM). Globalization and strategic sourcing expose supply chains to disruptions, making resilient sourcing strategies essential for adapting to fluctuations in supply and demand. This paper proposes a novel integrated hybrid model that combines multi-attribute decision-making

With the rapid development of intelligent inland waterway shipping and autonomous vessel, the development of inland waterway autonomous transportation has become a hot topic in the shipbuilding industry and the maritime field. This study comprehensively discusses the development trends and technical challenges of inland waterway autonomous transportation from three aspects: system architecture, advanced

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

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

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

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

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

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

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

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

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

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

Plant pests and diseases present a formidable challenge to the agri-food industry. However, by embracing the principles of sustainability, circular economy, intertwined supply networks, and implementing risk management strategies, these threats can be transformed into opportunities. In this paper, we address the issue of viability for the circular pomegranate supply chain (SC) under pest spread risk

Light Detection and Ranging (LiDAR), an advanced non-contact sensing method capable of capturing 3D spatial data with up to millimeter-level precision depending on the ranging method, has been widely used in pavement defect detection and road asset management. This paper provides an overview of LiDAR-based pavement inspection techniques in terms of measurement principles, characterization of acquisition

With the rapid development of Industrial Internet of Things (IIoT), the complexity of production environment has increased significantly. The flexible job-shop scheduling problem (FJSP) is often framed as a multi-objective optimization issue. However, as the scale and computational demands continue to grow, traditional multi-objective algorithms struggle to identify optimal scheduling policies. To

The proposed paper presents a methodology for mapping electronic manufacturing control processes within a Knowledge Management (KM) framework, aligning with human-centric and transdisciplinary approaches. Specifically, the paper explores a Proportional-Integral-Derivative (PID) process for tuning production machinery, facilitating quality management and predictive maintenance through an AI-driven model

Strategic decision-making in space mission selection is inherently complex, requiring a balance of multiple, often conflicting, quantitative and qualitative factors under uncertainty. This paper introduces a novel fuzzy analytical framework that extends the Strategic Assessment Model (SAM) by incorporating trapezoidal fuzzy numbers to evaluate mission alternatives systematically. By addressing the

Ensuring supply chain resilience (SCRES) in the face of unforeseen disruptions, such as natural disasters, geopolitical conflicts, or economic downturns, is a critical goal for decision-makers. While numerous SCRES frameworks have been proposed in existing literature, there is a lack of studies ranking these frameworks. Moreover, none of these frameworks fully satisfy all evaluation attributes. To

Despite the increased focus on data in Industry 4.0, textual data has received little attention in the production and engineering management literature. Data sources such as maintenance records and machine documentation usually are not used to help maintenance decision-making. Available studies mainly focus on categorizing maintenance records or extracting meta-data, such as time of failure, maintenance

In the domain of industrial control, supervisory control and data acquisition (SCADA) systems are essential for real-time monitoring and efficient data acquisition. However, as industrial systems grow in scale and complexity, conventional tag configuration methods face challenges in balancing precision and operational efficiency. Addressing these challenges requires innovative solutions. The rapid

The exponential growth of software complexity has led to a corresponding increase in software vulnerabilities, necessitating robust methods for automatic vulnerability detection and repair. This paper proposes DCodeBERT, a large language model (LLM) fine-tuned for vulnerability detection and repair in software code. Leveraging the pre-trained CodeBERT model, DCodeBERT is designed to understand both

Manufacturing is one of the industrial sectors taking benefit from the 4th industrial revolution and bringing existing production capacities closer to the ”factory of the future”. Quality, as a main concern in manufacturing, is also to benefit from this change of paradigm by introducing new key enabling technologies such as Internet of Things (IoT) and Artificial Intelligence (AI) into quality management

In contemporary practical scenarios, the integration of diverse renewable energy sources, such as solar, wind, hydro, biomass, geothermal, and energy storage solutions like batteries, presents complex challenges. These challenges demand simultaneous optimization of energy production, system reliability enhancement, and cost minimization including those related to fossil fuels and greenhouse gas emissions

Prefabricated construction is revolutionizing the industry by promoting efficiency and sustainability through off-site manufacturing and on-site assembly. Despite its potential, the digital transformation of prefabricated construction supply chains (PCSCs) has not kept pace with Industry 4.0 advancements, resulting in fragmented information and operational inefficiencies. Even more, limited attention

The basic principle of Industry 4.0 (I4.0) is the integration of digital systems and technologies to achieve complete automation and optimization. Despite the importance of integrating and interconnecting technologies according to I4.0 principles, the literature has mainly focused on the 'standalone' value of individual technologies while neglecting their potential network value. Given the high complexity

The popularity of Artificial Intelligence (AI) has risen sharply in recent years, revolutionizing applications in most sectors with unprecedented functionalities. Milestones and achievements like ChatGPT demonstrate not only the impressive capabilities of AI, but also how accessible such technologies have become in recent times. However, the success of AI applications depends heavily on the underlying

The global hospitality industry is undergoing a profound transformation, primarily driven by the internet era within the context of Industry 4.0 and the continuous evolution of the metaverse. The metaverse offers limitless possibilities for creating immersive and personalized accommodation experiences by closely integrating the virtual with the real world. It also provides a new perspective and mindset

Building Information Modeling (BIM) relies on standardized ontologies like IfcOWL to address interoperability. However, the increasing complexity and diversity of construction information requirements demand automated enrichment of BIM ontologies, which is hindered by several factors, including complexity in ontology structure, scalability limitations, and domain-specific issues. Manual curation and

Multi-robot collaboration is a crucial execution tool for mixed-model assembly lines. The rapid reconfiguration of the robots with impaired skills to maintain the robustness of the assembly line remains a significant challenge. With a focus on knowledge-driven faster transition technologies for collaborative robots, this paper proposes a Knowledge Sharing-enabled Low-code Program (KSLC) method to address

The effective management of product information within a formalized, digital and interoperable infrastructure remains a significant gap in realizing the full potential of modern Product Lifecycle Management (PLM) implementations in industrial contexts. While the academic paradigm of PLM has been extensively emphasized in the scientific literature for over two decades as a sustainable company strategy

Cloud users rent virtual machines (VMs) with varying parameters tailored to their unique business requirements. These diverse VM parameters add complexity to data center (DC) management strategies. Among the crucial parameters are CPU and memory, which must be optimized to ensure efficient physical resource utilization and decreased DC energy consumption. This article proposes three algorithms to manage

The construction industry's digitalization produces a large volume of data from sources like Building Information Modeling (BIM), IoT sensors, drones, real-time project monitoring, and resource tracking. However, only 1-2 % of this data is effectively utilized due to limitations in processing, analysis, and integration across platforms. These limitations are influenced by micro-level factors like syntactics

Quality 4.0, a modern approach to quality management, applies Industry 4.0 principles to enhance product and service quality. By utilizing data-driven techniques like predictive analytics, machine learning, and artificial intelligence, Quality 4.0 aims to improve traditional quality management systems. Higher education institutions (HEIs) can benefit from Quality 4.0 by employing data-driven decision-making

Smart cities can create a connected and efficient urban environment by integrating advanced technologies with mobile robots. Mobile robots play a significant role in various smart city applications, ranging from transportation and logistics to surveillance and maintenance tasks, and have the potential to revolutionize the way people live and work in smart cities. Ensuring the efficient, greener and

In this study, a new technique of Z-number preference ranking by similarity to the ideal solution is proposed for estimating risk in failure mode and effects analysis. The method ranks all identified faults using subjective and objective weights of risk factors. The subjective weights are calculated by the Z-number analytical hierarchy process, and the objective weights are calculated using the Z-number

Disassembly lines are an effective means for the large-scale, industrialized recycling of end-of-life products. Among these, U-shaped disassembly lines are particularly noted for their combination of flexibility and production efficiency. This study addresses the U-shaped disassembly line balancing problem, considering the coexistence of separate stations and spatial limitations within workstations

With the advancement of digitalization and intelligence, the demand for improving processing quality and efficiency is becoming increasingly urgent. Digital twin technology, a key supporting technology for intelligent manufacturing, can accurately simulate and predict the machining process in virtual space. This is achieved through data fusion analysis and iterative optimization, effectively ensuring

Failure mode and effects analysis (FMEA) is an essential tool for mitigating potential failures, particularly during the ramp-up phases of new products. However, its effectiveness is often limited by the reasoning capabilities of the FMEA tools, which are usually tabular structured. Meanwhile, large language models (LLMs) offer novel prospects for advanced natural language processing tasks. However

The existing limitations of the fundamental laws necessary for constructing a comprehensive and widely accepted theoretical framework have significantly hindered the progress of Information Management. This lack has resulted in a predominant reliance on indirect strategies to address information management challenges, often leading to complex, inefficient, and somewhat stochastic analyses and evaluations

Gomez-Flores et al. proposed a Long Short-Term Memory Neural Network (LSTM-NN) for predicting the flotation behavior of battery active materials using various physicochemical and hydrodynamic variables. While they achieved high prediction accuracy, validated through Mean Relative Error (MRE) and Mean Squared Error (MSE) metrics, concerns arise regarding the integrity of feature importance assessments

Skilled labor shortages and the growing trend for customized products are increasing the complexity of manufacturing systems. Automation is often proposed to address these challenges, but industries operating under the engineer-to-order, lot-size-one production model often face significant limitations due to the lack of relevant data. This study investigates an approach for the extraction of assembly-relevant

Collaborative logistics has been widely recognised as an effective avenue to reduce carbon emissions by enhanced truck utilisation and reduced travel distance. However, stakeholders’ participation in collaborations is hindered by information-sharing barriers and absence of integrated systems. We, thus, in this paper addresses these barriers by investigating an integrated platform that facilitates collaboration

In this work we tackle the challenge of enhancing the quality of analog recorded images in real-time. This involves two key aspects: super-resolution to improve visual detail, and artifact removal to address specific issues unique to analog footage. We propose ARENet, a memory-efficient architecture trained in an adversarial setting that can handle analog videos with VHS-like artifacts while maintaining

The rapid advancement and incorporation of Artificial Intelligence (AI) and the Internet of Things (IoT) have created exceptional opportunities to revolutionize healthcare and treatment methods and offer significant potential for broader industrial information integration. Nevertheless, the growth of intelligent healthcare systems faces challenges such as data confidentiality concerns and the safety