Search Results (326)

This study investigates the role of Big Data Analytics Capabilities (BDACs) in ambidexterity explorative innovation (EXPLRI) and exploitative (EXPLOI) innovation for achieving a sustainable performance (SP) in the manufacturing sector of a resource-constrained developing economy. While a BDAC has been widely linked to innovation in developed economies, its effectiveness in developing contexts shaped by indigenous innovation practices like Jugaad remains underexplored. Anchored in the Resource-Based View (RBV) and Dynamic Capabilities (DC) theory, we propose a model where the BDAC enhances both EXPLRI and EXPLOI, which subsequently leads to an improved sustainable performance. We further examine the Jugaad capability as a cultural moderator. Using survey data from 418 manufacturing firms and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM), results confirm that BDA capabilities significantly boost both types of innovations, which positively impact sustainable performance dimensions. Notably, Jugaad positively moderates the relationship between EXPLOI and financial, innovation, and operational performance but negatively moderates the link between EXPLRI and innovation performance. These findings highlight the nuanced influence of culturally embedded innovation practices in BDAC-driven ecosystems. This study contributes by extending the RBV–DC framework to include cultural innovation capabilities and empirically validating the contingent role of Jugaad in enhancing or constraining innovation outcomes. This study also validated the Jugaad capability measurement instrument for the first time in the context of Pakistan. For practitioners, aligning data analytics strategies with local innovative cultures is vital for sustainable growth in emerging markets. Full article

The exponential increase in textual data generated across sectors such as healthcare, finance, and smart manufacturing has intensified the need for effective Big Data analytics. Large language models (LLMs) have become critical tools because of their advanced language processing capabilities. However, their static nature limits their ability to incorporate real-time and domain-specific knowledge. Retrieval-augmented generation (RAG) addresses these limitations by enriching LLM outputs through external content retrieval. Nevertheless, traditional RAG systems remain inefficient, often exhibiting high retrieval latency, redundancy, and diminished response quality when scaled to large datasets. This paper proposes an innovative structured RAG framework specifically designed for large-scale Big Data analytics. The framework transforms unstructured partial prompts into structured semantically coherent partial prompts, leveraging element-specific embedding models and dimensionality reduction techniques, such as principal component analysis. To further improve the retrieval accuracy and computational efficiency, we introduce a multi-level filtering approach integrating semantic constraints and redundancy elimination. In the experiments, the proposed method was compared with structured-format RAG. After generating prompts utilizing two methods, silhouette scores were computed to assess the quality of embedding clusters. The proposed method outperformed the baseline by improving the clustering quality by 32.3%. These results demonstrate the effectiveness of the framework in enhancing LLMs for accurate, diverse, and efficient decision-making in complex Big Data environments. Full article

The field of metal additive manufacturing has witnessed significant growth in recent years, with technology offering the ability to produce complex geometries that are challenging to manufacture using the traditional methods. In situ monitoring and control of the manufacturing process are crucial for increasing the production capacity and improving the quality of manufactured parts. This article provides a comparative analysis of computational, indirect, and direct methods for in situ temperature monitoring during additive manufacturing of metal alloy components. Furthermore, it discusses the current status, recent improvements, and perspectives for in situ temperature measurements. The basic principles of thermal imaging, two-color pyrometry, and millimeter-wave radiometry are explored, highlighting their limitations for addressing challenges related to material emissivity and rapid changes in building material composition. Overcoming the challenges related to the inaccessibility of the chamber where the parts are formed, direct temperature measurements would allow for the integration of collected information into big data systems. Within the framework of Industry 4.0, this approach offers a viable alternative to the conventional metal shaping processes, improving the production capacity and part quality. This research aims to contribute to ongoing advancements in metal additive manufacturing and its potential to completely replace traditional metal casting practices in the Industry 4.0 era. Full article

Amid the accelerated digital transformation and with the growing demand for smart manufacturing, the applications of the Internet of Things (IoT) and total quality management (TQM) have attracted increasing attention. Using R for bibliometric analysis, we explored research trends in IoT and TQM in terms of digital transformation and smart manufacturing. Data were gathered from the Web of Science from 1998 to 2025, with a total of 787 publications from 265 sources involving 2326 authors. A total of 31% of the authors collaborated internationally, indicating global interest in this topic. The publications had 33.65 citations on average, totaling 33,599 citations. Wang L.H. and Tao F. were identified as important authors. Keywords of “Industry 4.0”, “cyber-physical systems”, and “big data” underscore the technological significance of IoT and TQM. Major journals such as the Journal of Manufacturing Systems and IEEE Access had notable academic influence. Co-citation analysis results revealed that IoT and TQM played a significant role in driving digital transformation and enhancing production efficiency, offering references for enterprises in strategic planning for smart manufacturing. Full article

Despite advances in automation, the global manufacturing sector continues to rely heavily on manual package inspection, creating bottlenecks in production and increasing labor demands. Although disruptive technologies such as big data analytics, smart sensors, and machine learning have revolutionized industrial connectivity and strategic decision-making, real-time quality control (QC) on conveyor lines remains predominantly analog. This study proposes an intelligent package integrity monitoring system that integrates waste reduction strategies with both narrow and Generative AI approaches. Narrow AI models were deployed to detect package damage at full line speed, aiming to minimize manual intervention and reduce waste. Using a synthetically generated dataset of 200 paired top-and-side package images, we developed and evaluated 10 distinct detection pipelines combining various algorithms, image enhancements, model architectures, and data processing strategies. Several pipeline variants demonstrated high accuracy, precision, and recall, particularly those utilizing a YOLO v8 segmentation model. Notably, targeted preprocessing increased top-view MobileNetV2 accuracy from chance to 67.5%, advanced feature extractors with full enhancements achieved 77.5%, and a segmentation-based ensemble with feature extraction and binary classification reached 92.5% accuracy. These results underscore the feasibility of deploying AI-driven, real-time QC systems for sustainable and efficient manufacturing operations. Full article

Digital twins, as part of Industry 4.0, are critical for advanced smart manufacturing processes, including machining. Sensor systems in smart manufacturing allow for real-time tracking of all changes in the machining process as well as simulation of an object’s behavior in the real world. It can also intervene and correct any defects that may arise during the machining process. The current review covers basic concepts for machining processes for the first time in detail, including Big Data, the Internet of Things, product lifecycle management, continuous acquisition and lifecycle support, machine learning, digital twin prototypes, digital twin instances, digital twin aggregates, and digital twin environments. The review article examines digital twins for the most common machining processes, such as turning, milling, drilling, and grinding. This review also highlights the benefits and drawbacks, as well as the prospects for using digital twins in smart manufacturing. Full article

With the development of intelligent manufacturing technology, the manufacturing industry is gradually realizing intelligent production. Especially for metal cutting with extremely complex processes, it is of great significance to realize intelligence. Taking the cutting process of aero-engine typical low-rigidity parts as the main line, this article builds an intelligent processing architecture based on a big data platform, which includes customized design of cutting tools, intelligent optimization of cutting parameters, simulation of cutting conditions, and online monitoring and control of cutting processes. At the same time, the realization of related key technologies is explained. Then, this article introduces in detail the intelligent decision-making process based on deep learning, the customized tool design process based on structural features, the simulation process of cutting based on geometric features of parts, as well as the monitoring and control process of Numerical Control (NC) machining based on condition perception. In addition, based on the processing requirements and difficulties of specific parts, formulate a specific intelligent implementation plan under this processing mode. Through the implementation of the above architecture and key technologies, the cutting processing system can automatically optimize the cutting parameters according to real-time working conditions and adjust its own cutting conditions. At the same time, machine tool condition, cutting tool condition, and low-rigidity part condition are real-time monitored to achieve high-precision, efficient, intelligent, and precise cutting of low-rigidity parts. The proposed architecture can provide a reference model for the research and application of intelligent cutting technology for low-rigidity parts. Full article

Facility layout design (FLD) is critical for optimizing manufacturing efficiency, yet traditional approaches struggle with complexity, dynamic constraints, and fragmented data integration. This study proposes a generative-AI-enabled facility layout design, a novel paradigm aligning with Industry 4.0, to address these challenges by integrating generative artificial intelligence (AI), semantic models, and data-driven optimization. The proposed method evolves from three historical paradigms: experience-based methods, operations research, and simulation-based engineering. The metamodels supporting the generative-AI-enabled facility layout design is the Asset Administration Shell (AAS), which digitizes physical assets and their relationships, enabling interoperability across systems. Domain-specific knowledge graphs, constructed by parsing AAS metadata and enriched by large language models (LLMs), capture multifaceted relationships (e.g., spatial adjacency, process dependencies, safety constraints) to guide layout generation. The convolutional knowledge graph embedding (ConvE) method is employed for link prediction, converting entities and relationships into low-dimensional vectors to infer optimal spatial arrangements while addressing data sparsity through negative sampling. The proposed reference architecture for generative-AI-enabled facility layout design supports end-to-end layout design, featuring a 3D visualization engine, AI-driven optimization, and real-time digital twins. Prototype testing demonstrates the system’s end-to-end generation ability from requirement-driven contextual prompts and extensively reduced complexity of modeling, integration, and optimization. Key innovations include the fusion of AAS with LLM-derived contextual knowledge, dynamic adaptation via big data streams, and a hybrid optimization approach balancing competing objectives. The 3D layout generation results demonstrate a scalable, adaptive solution for storage workshops, bridging gaps between isolated data models and human–AI collaboration. This research establishes a foundational framework for AI-driven facility planning, offering actionable insights for AI-enabled facility layout design adoption and highlighting future directions in the generative design of complex engineering. Full article

The integration of Industry 4.0 technologies with Corporate Social Responsibility (CSR) initiatives offers transformative potential for enhancing safety culture in manufacturing. This study investigates how managerial competence facilitates the alignment of tools like the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics with CSR principles to foster sustainable safety practices. Employing a qualitative methods approach with secondary data from 2010 to 2024, including case studies of some of five leading firms (Siemens, General Electric, Toyota, Bosch, and Ford) and a systematic literature review, this analysis uses thematic and statistical techniques. The results show that strategic integration significantly reduces workplace hazards by 30–50%, boosts employee engagement, and enhances operational efficiency through real-time monitoring, predictive maintenance, and CSR alignment. Managerial competence, encompassing strategic vision, technical expertise, and stakeholder engagement, is critical for aligning these domains, delivering enhanced safety, sustainability, and competitive advantages. Full article

This systematic review examines the critical intersection of digital transformation, supply chain resilience, and sustainability within manufacturing contexts, with specific implications for Saudi Arabian industries. Through a comprehensive analysis of 124 peer-reviewed articles published between 2018 and 2024, we identify how emerging technologies—including Internet of Things (IoT), artificial intelligence, blockchain, and big data analytics—transform traditional supply chains into dynamic ecosystems capable of withstanding disruptions while advancing sustainability goals. Our findings reveal that digital transformation positively influences both resilience and sustainability outcomes. Still, these relationships are significantly moderated by three key factors: supply chain dynamism, regulatory uncertainty, and integration of innovative technologies. The study demonstrates that while high supply chain dynamism amplifies the positive effects of digital technologies on resilience capabilities, regulatory uncertainty creates implementation barriers that potentially diminish these benefits. Moreover, successfully integrating innovative technologies is a critical mediating mechanism translating digital initiatives into tangible sustainability improvements. The review synthesises these findings into an integrated conceptual framework that captures the complex interrelationships between these domains and provides specific strategic recommendations for Saudi Arabian manufacturing organisations. By addressing the identified research gaps—particularly the lack of industry-specific investigations in emerging economies—this review offers valuable insights for researchers and practitioners seeking to leverage digital transformation for simultaneously efficient, resilient, and sustainable supply chain operations in rapidly evolving business environments. Full article

Programmatic buying has attracted growing interest from manufacturers and has become a driving force behind the growth of digital advertising. Among various formats, mobile advertisements (ads) have emerged as a preferred choice over traditional ones due to their advanced automation, adaptability, and cost-effectiveness. Despite their increasing adoption, academic research on mobile ads remains relatively limited. Unlike conventional statistical analysis techniques, the proposed feature selection methods eliminate the need for assumptions related to data properties such as independence, normal distribution, and constant variance in regression. Additionally, feature selection techniques have recently gained traction in big data analysis, addressing the limitations inherent in traditional statistical approaches. Consequently, this study aims to determine the key success factors of mobile ads in fostering customer loyalty, offering advertisers valuable insights for optimizing mobile ad design. This study begins by identifying potential factors influencing mobile advertising effectiveness. Then, it applies Support Vector Machine Recursive Feature Elimination (SVM-RFE), correlation-based selection, and consistency-based selection methods to determine the key drivers of customer retention. The findings reveal that “Price” and “Preference” are the most significant contributors to enhancing repurchase intention. Moreover, factors such as “Language”, “Perceived Usefulness”, “Interest”, “Mobile Device”, and “Informativeness” are also essential in maximizing the effectiveness of mobile advertising. Full article

This paper introduces an acoustic-based monitoring system for high-speed CNC drilling, aimed at optimizing processes and enabling real-time machine state detection. High-fidelity acoustic sensors capture sound signals during drilling operations, allowing the identification of critical events such as tool engagement, material breakthrough, and tool withdrawal. Advanced signal processing techniques, including spectrogram analysis and Fast Fourier Transform, extract dominant frequencies and acoustic patterns, while machine learning algorithms like DBSCAN clustering classify operational states such as cutting, breakthrough, and returning. Experimental studies on materials including acrylic, PTFE, and hardwood reveal distinct acoustic profiles influenced by material properties and drilling conditions. Smoother sound patterns and lower dominant frequencies characterize PTFE drilling, whereas hardwood produces higher frequencies and rougher patterns due to its density and resistance. These findings demonstrate the correlation between acoustic emissions and machining dynamics, enabling non-invasive real-time monitoring and predictive maintenance. As AI power increases, it is expected to extract in-situ process information and achieve higher resolution, enhancing precision in data interpretation and decision-making. A key contribution of this project is the creation of an open sound library for drilling processes, fostering collaboration and innovation in intelligent manufacturing. By integrating big data concepts and intelligent algorithms, the system supports continuous monitoring, anomaly detection, and process optimization. This AI-ready hardware enhances the accuracy and efficiency of drilling operations, improving quality, reducing tool wear, and minimizing downtime. The research establishes acoustic monitoring as a transformative approach to advancing CNC drilling processes and intelligent manufacturing systems. Full article

Background: The global climate crisis has intensified the demand for sustainable solutions, positioning Reverse Logistics (RL) as a critical strategy for minimizing environmental impacts. Simultaneously, Industry 4.0 technologies are transforming RL operations by enhancing their collection, transportation, storage, sorting, remanufacturing, recycling, and disposal processes. Understanding the roles of these technologies is essential for improving efficiency and sustainability. Methods: This study employs a systematic literature review, following the PRISMA methodology, to identify key Industry 4.0 technologies applicable to RL. Publications from Scopus and Web of Science were analyzed, leading to the development of a theoretical framework linking these technologies to RL activities. Results: The findings highlight the fact that technologies like the Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics, Cloud Computing, and Blockchain enhance RL by improving traceability, automation, and sustainability. Their application optimizes execution time, reduces operational costs, and mitigates environmental impacts. Conclusions: For the transportation and manufacturing sectors, integrating Industry 4.0 technologies into RL can streamline supply chains, enhance decision-making, and improve resource utilization. Smart tracking, predictive maintenance, and automated sorting systems reduce waste and improve operational resilience, reinforcing the transition toward a circular economy. By adopting these innovations, stakeholders can achieve economic and environmental benefits while ensuring regulatory compliance and long-term competitiveness. Full article

Big data analytics has the potential to greatly improve the operations of manufacturing industries, aid in decision making, and foster innovation. However, there exist several barriers that undermine the successful adoption of big data analytics in these industries. This paper presents a structural analysis of the barrier to big data analytics adoption in manufacturing industries. Through an extensive literature review and expert analysis, a compilation of the various barriers was made. The interpretive structure modeling (ISM) technique was then used to analyze the interplay between the barriers: this technique was used to build a hierarchy whose respective objective functions indicated how each barrier influenced the other. These findings help in the understanding of the hierarchical relationships between the various barriers and can thus help organizations in prioritizing strategies to mitigate these barriers. The results depict some barriers which do have a high-power influence over others and, as such, depict critical points that manufacturing industries need to address when adopting big data analytics. This paper also elaborates the relationships between the barriers, which will help the decision makers create strategies to mitigate them effectively. This study’s findings contribute to the existing body of knowledge on barriers to adopting big data analytics in manufacturing industries and provides an efficient approach for organizations to systematically address barriers. Full article

The integration of Industry 4.0 technologies, Circular Economy (CE) principles, and Green Human Resource Management (GHRM) offers transformative potential to address global sustainability challenges. Industry 4.0, characterized by advanced digital technologies like IoT, Additive Manufacturing (AM), and Big Data Analytics (BDAA), enhances operational efficiency, resource optimization, and waste minimization. Concurrently, CE redefines economic models through resource conservation, lifecycle extension, and reduced environmental impact, supported by frameworks like ReSOLVE. GHRM aligns human resource practices with sustainability objectives, fostering Green behaviors and embedding environmental considerations into organizational culture. Despite the individual benefits of these frameworks, their combined application remains underexplored, with limited research on their systemic integration. This study addresses this gap by examining the synergies between Industry 4.0 technologies, CE principles, and GHRM strategies, identifying opportunities and challenges in their implementation. A theoretical model is proposed, emphasizing systemic innovation, resource efficiency, and collaborative value chains as key enablers of sustainable development. The model highlights the necessity of aligning technological advancements with human-centric approaches to overcome behavioral, organizational, and infrastructural barriers in transitioning toward sustainability. The findings offer practical insights for policymakers and industry leaders, outlining strategies for integrating Industry 4.0 with CE and GHRM to drive sustainability transitions. By synthesizing technological, environmental, and human resource dimensions, this research contributes both theoretically and practically, positioning organizations to enhance sustainability while maintaining competitiveness in evolving economic landscapes. Full article