Search Results (12,374)

Discrete-event simulation (DES) as an operations research (OR) technique has continued to evolve since its inception in the 1950s. DES evolution mirrors the advances in computer science (hardware and software, processing speed, and advanced information visualization capabilities). DES overcame the initial usability obstacles and lack of efficacy challenges in the early 2000s to remain a popular OR tool of “last resort.” Using bibliographic data from SCOPUS, this study undertakes a science mapping of the DES literature and evaluates its evolution and expansion in the past fifteen years. The results show asymmetrical but positive yearly literature output; broadened DES adoption in diverse fields; and sustained relevance as a potent OR method for tackling old, new, and emerging operations and production issues. The thematic analysis identifies DES as an essential tool that integrates and enhances digital twin technology in Industry 4.0, playing a central role in enabling digital transformation processes that have swept the industrial space in manufacturing, logistics, healthcare, and other sectors. DES integration with generative/artificial intelligence (GenAI/AI) provides a great potential to revolutionize modeling and simulation activities, tasks, and processes. Future studies will explore more ways to integrate GenAI tools in DES. Full article

This paper examines the role of internal knowledge in driving innovation and firm performance in sub-Saharan Africa, using panel data from the World Bank Enterprise Surveys covering fifteen countries in the region. Specifically, the analysis assesses the extent to which internal knowledge, measured through employee educational attainment, stimulates innovation, and whether innovation, in turn, contributes to improved firm performance. The findings reveal that internal knowledge has a significant positive effect on innovation, and that both internal knowledge and innovation are key drivers of firm performance in developing country contexts. These results underscore the strategic importance of building firm-level knowledge capabilities to enhance competitiveness, particularly among manufacturing firms. The study offers valuable policy implications, emphasizing the need to strengthen internal learning systems, workforce skills, and innovation support mechanisms to foster inclusive industrial growth in sub-Saharan Africa. Full article

The present paper investigates the adoption of green manufacturing approaches among industrial enterprises in Slovakia, emphasizing the interplay between institutional pressures and enterprise-level resources. Based on a survey of 88 enterprises from energy- and material-intensive sectors, the study evaluates how regional context and enterprise size influence the adoption of green practices. Using logistic regression and the chi-squared test, the findings reveal minimal regional variation, suggesting strong isomorphic effects of harmonised European Union environmental regulations. In contrast, enterprise size significantly correlates with the adoption of complex green practices, confirming the relevance of the resource-based view. These results highlight the dominance of internal capabilities over regional factors in green transition pathways within small post-transition economies. The study contributes to cross-national theorising by showing how resource asymmetries, rather than institutional diversity, shape environmental behaviour in uniform regulatory environments. Specifically, the paper examines how institutional pressures, enterprise-level resources, stakeholders, and legitimacy influence the adoption of green manufacturing practices in Slovak industrial enterprises. The study draws on institutional theory, the resource-based view, stakeholder theory, and legitimacy theory to explore the relationship between enterprise size, regional location, and the adoption levels of green manufacturing. Full article

The rapid development of digital technology is reshaping the global economic landscape. However, its impact on firms’ capacity utilization rate (CUR), particularly through technological innovation, remains unclear. This study investigates this issue by developing an endogenous growth model that connects digital technology to CUR. The empirical analysis is based on data from Chinese A-share manufacturing firms. The methods employed include quantile regression, instrumental variable techniques, and various tests to explore underlying mechanisms. CUR is calculated using a special model that looks at random variations, and digital transformation is assessed using text analysis powered by machine learning. The findings indicate that digital transformation significantly enhances CUR, especially for firms with average capacity utilization levels, but has a limited effect on low- and high-end firms. Moreover, technological innovation mediates this relationship; however, factors like “double arbitrage” (involving policy and capital markets) and “herd effects” tend to prioritize quantity over quality, which constrains innovation potential. Improvements in CUR lead to enhanced firm performance and productivity, generating industry spillovers and demonstrating the broader economic externalities of digitalization. This study uniquely applies endogenous growth theory to examine the role of digital transformation in optimizing CUR. It introduces the “quantity-quality” technology innovation paradox as a crucial mechanism and highlights industry spillovers to address overcapacity while offering insights for fostering sustainable economic and social development in emerging markets. Full article

Background: The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies have combined end-to-end data analytics and data mining methods to proactively predict and mitigate such failures. This study aims to develop and validate a comprehensive framework combining data analytics and data mining to prevent machine failures and support decision-making in a metal–mechanical manufacturing environment. Methods: First, exploratory data analytics were performed on the sensor and logistics data to identify significant relationships and trends between variables. Next, a preprocessing pipeline including data cleaning, data transformation, feature selection, and resampling was applied. Finally, a decision tree model was trained to identify conditions prone to failures, enabling not only predictions but also the explicit representation of knowledge in the form of decision rules. Results: The outstanding performance of the decision tree (82.1% accuracy and a Kappa index of 78.5%), which was modeled from preprocessed data and the insights produced by data analytics, demonstrates its ability to generate reliable rules for predicting failures to support decision-making. The implementation of the proposed framework enables the optimization of predictive maintenance strategies, effectively reducing unplanned downtimes and enhancing the reliability of production processes in the metal–mechanical industry. Full article

The present-day issues related to the cotton-growing industry, namely yield estimation, pest effect, and growth phase diagnostics, call for integrated, scalable monitoring solutions. This write-up reveals Cotton Multitask Learning (CMTL), a transformer-driven multitask framework that launches three major agronomic tasks from UAV pictures at one go: boll detection, pest damage segmentation, and phenological stage classification. CMTL does not change separate pipelines, but rather merges these goals using a Cross-Level Multi-Granular Encoder (CLMGE) and a Multitask Self-Distilled Attention Fusion (MSDAF) module that both allow mutual learning across tasks and still keep their specific features. The biologically guided Stage Consistency Loss is the part of the architecture of the network that enables the system to carry out growth stage transitions that occur in reality. We executed CMTL on a tri-source UAV dataset that fused over 2100 labeled images from public and private collections, representing a variety of crop stages and conditions. The model showed its virtues state-of-the-art baselines in all the tasks: setting 0.913 mAP for boll detection, 0.832 IoU for pest segmentation, and 0.936 accuracy for growth stage classification. Additionally, it runs at the fastest speed of performance on edge devices such as NVIDIA Jetson Xavier NX (Manufactured in Shanghai, China), which makes it ideal for deployment. These outcomes evoke CMTL’s promise as a single and productive instrument of aerial crop intelligence in precision cotton agriculture. Full article

An in-depth investigation into the evolutionary characteristics, transmission mechanisms, and optimization pathways of electricity consumption linkages across China’s industrial sectors highlights their substantial theoretical and practical significance in achieving the “dual carbon” goals and advancing high-quality economic development. This study investigates the structural characteristics and developmental trends of electricity consumption linkages across China’s industrial sectors using an enhanced hypothetical extraction method. The analysis draws on national input–output tables and sector-specific electricity consumption data during the period from 2002 to 2020. Key transmission routes between industrial sectors are identified through path analysis and average path length calculations. The findings reveal that China’s industrial electricity consumption structure is marked by notable scale expansion and differentiation. The magnitude of inter-sectoral electricity flows continues to grow steadily. The evolution of these linkages exhibits clear phase-specific patterns, while the intensity of electricity consumption connections across sectors shows pronounced heterogeneity. Furthermore, the transmission path analysis revealed differentiated characteristics of electricity influence transmission, with generally shorter internal paths within sectors, significant cross-sectoral transmission differences, and manufacturing demonstrating good transmission accessibility with moderate path distances to major sectors. These insights provide a robust foundation for designing differentiated energy conservation policies, as well as for optimizing the overall structure of industrial electricity consumption. Full article

The increasing complexity and cost of manufacturing monolithic chips have driven the semiconductor industry toward chiplet-based designs, where smaller, modular chiplets are integrated onto a single interposer. While chiplet architectures offer significant advantages, such as improved yields, design flexibility, and cost efficiency, they introduce new security challenges in the horizontal hardware manufacturing supply chain. These challenges include risks of hardware Trojans, cross-die side-channel and fault injection attacks, probing of chiplet interfaces, and intellectual property theft. To address these concerns, this paper presents ChipletQuake, a novel on-chiplet framework for verifying the physical security and integrity of adjacent chiplets during the post-silicon stage. By sensing the impedance of the power delivery network (PDN) of the system, ChipletQuake detects tamper events in the interposer and neighboring chiplets without requiring any direct signal interface or additional hardware components. Fully compatible with the digital resources of FPGA-based chiplets, this framework demonstrates the ability to identify the insertion of passive and subtle malicious circuits, providing an effective solution to enhance the security of chiplet-based systems. To validate our claims, we showcase how our framework detects hardware Trojans and interposer tampering. Full article

Anomaly detection in industrial imaging is critical for ensuring quality and reliability in automated manufacturing processes. While recently several methods have been reported in the literature that have demonstrated impressive detection performance on standard benchmarks, they necessarily rely on computationally intensive CNN architectures and post-processing techniques, necessitating access to high-end GPU hardware and limiting practical deployment in resource-constrained settings. In this study, we introduce a novel anomaly detection framework that leverages feature maps from a lightweight convolutional neural network (CNN) backbone, MobileNetV2, and cascaded detection to achieve notable accuracy as well as computational efficiency. The core of our method consists of two main components. First is a PCA-based anomaly detection module that specifically exploits near-zero variance features. Contrary to traditional PCA methods, which tend to focus on the high-variance directions that encapsulate the dominant patterns in normal data, our approach demonstrates that the lower variance directions (which are typically ignored) form an approximate null space where normal samples project near zero. However, the anomalous samples, due to their inherent deviations from the norm, lead to projections with significantly higher magnitudes in this space. This insight not only enhances sensitivity to true anomalies but also reduces computational complexity by eliminating the need for operations such as matrix inversion or the calculation of Mahalanobis distances for correlated features otherwise needed when normal behavior is modeled as Gaussian distribution. Second, our framework consists of a cascaded multi-stage decision process. Instead of combining features across layers, we treat the local features extracted from each layer as independent stages within a cascade. This cascading mechanism not only simplifies the computations at each stage by quickly eliminating clear cases but also progressively refines the anomaly decision, leading to enhanced overall accuracy. Experimental evaluations on MVTec and VisA benchmark datasets demonstrate that our proposed approach achieves superior anomaly detection performance (99.4% and 91.7% AUROC respectively) while maintaining a lower computational overhead compared to other methods. This framework provides a compelling solution for practical anomaly detection challenges in diverse application domains where competitive accuracy is needed at the expense of minimal hardware resources. Full article

Polyetheretherketone (PEEK) is a semi-crystalline thermoplastic polymer which, due to its very high mechanical properties and high chemical resistance, has found application in the automotive, aerospace, chemical, food and medical (biomedical engineering) industries. Owing to the use of additive technologies, particularly the Fused Filament Fabrication (FFF) method, this material is the most widely used plastic to produce skull reconstruction implants, parts of dental implants and orthopedic implants, including spinal, knee and hip implants. PEEK enables the creation of personalized implants, which not only have greater elasticity compared to implants made of metal alloys but also resemble the physical properties of the cortical layer of human bone in terms of their mechanical properties. Therefore, the aim of this article is to characterize polyether ether ketone as an alternative material used in the manufacturing of implants in orthopedics and dentistry. Full article

Industries 4.0 and 5.0 are based on technological advances, notably large language models (LLMs), which are making a significant contribution to the transition to smart factories. Although considerable research has explored this phenomenon, the literature remains fragmented and lacks an integrative framework that highlights the multifaceted implications of using LLMs in the context of digital manufacturing. To address this limitation, we conducted a systematic literature review, analyzing 53 papers selected according to predefined inclusion and exclusion criteria. Our descriptive and thematic analyses, respectively, mapped new trends and identified emerging themes, classified into three axes: (1) manufacturing process optimization, (2) data structuring and innovation, and (3) human–machine interaction and ethical challenges. Our results revealed that LLMs can enhance operational performance and foster innovation while redistributing human roles. Our research offers an in-depth understanding of the implications of LLMs. Finally, we propose a future research agenda to guide future studies. Full article

Magnetron Sputter Vacuum Deposition (MSVD) has undergone significant advancements since its inception. This review explores the evolution of MSVD, encompassing its fundamental principles, various techniques (including reactive sputtering, pulsed magnetron sputtering, and high-power impulse magnetron sputtering), and its wide-ranging industrial applications. While detailing the advantages of high deposition rates, versatility in material selection, and precise control over film properties, the review also addresses inherent challenges such as low target utilization and plasma instability. A significant portion focuses on the crucial role of MSVD in the automotive industry, highlighting its use in creating durable, high-quality coatings for both aesthetic and functional purposes. The transition from traditional electroplating methods to more environmentally friendly MSVD techniques is also discussed, emphasizing the growing demand for sustainable manufacturing processes. This review concludes by summarizing the key advancements, remaining challenges, and potential future trends in magnetron sputtering technologies. Full article

Decarbonization of the Greek economy requires significant investments in clean technologies. This will boost demand for goods and services and will create multiplier effects on output value added and employment, though reliance on imported technologies might increase the trade deficit. This study employs input–output analysis to estimate the direct, indirect, and multiplier effects of green transition investments on Greek output, value added, employment, and imports across five-year intervals from 2025 to 2050. Two scenarios are considered: the former is based on the National Energy and Climate Plan (NECP), driven by a large-scale exploitation of RES and technologies promoting electrification of final demand, while the latter (developed in the context of the CLEVER project) prioritizes energy sufficiency and efficiency interventions to reduce final energy demand. In the NECP scenario, GDP increases by 3–10% (relative to 2023), and employment increases by 4–11%. The CLEVER scenario yields smaller direct effects—owing to lower investment levels—but larger induced impacts, since energy savings boost household disposable income. The consideration of three sub-scenarios adopting different levels of import-substitution rates in key manufacturing sectors exhibits pronounced divergence, indicating that targeted industrial policies can significantly amplify the domestic economic benefits of the green transition. Full article

This study evaluates the effectiveness of the B Impact Assessment (BIA) as a catalyst for integrating sustainability into industrial firms through a qualitative case study of LC Paper, the first B Corp-certified tissue manufacturer globally and a pioneer in applying BIA in the pulp and paper sector. Based on semi-structured interviews, organizational documents, and direct observation, this study examines how BIA influences corporate governance, environmental practices, and stakeholder engagement. The findings show that BIA fosters structured goal setting and the implementation of measurable actions aligned with environmental stewardship, social responsibility, and economic resilience. Tangible outcomes include improved stakeholder trust, internal transparency, and employee development, while implementation challenges such as resource allocation and procedural complexity are also reported. Although the single-case design limits generalizability, this study identifies mechanisms transferable to other firms, particularly those in environmentally intensive sectors. The case studied also illustrates how leadership commitment, participatory governance, and data-driven tools facilitate the operationalization of sustainability. By integrating stakeholder and institutional theory, this study contributes conceptually to understanding certification frameworks as tools for embedding sustainability. This research offers both theoretical and practical insights into how firms can align strategy and impact, expanding the application of BIA beyond early adopters and into traditional industrial contexts. Full article

This study explores whether sustainability achievements—proxied through ESG (environmental, social, and governance) reporting—are associated with superior financial performance in Latvia’s manufacturing sector, where ESG maturity remains low and institutional readiness is still emerging. Building on stakeholder, legitimacy, signal, slack resources, and agency theories, this study applies a mixed-method approach (that consists of two analytical stages) suited to the limited availability and reliability of ESG-related data in the Latvian manufacturing sector. Financial indicators from three large firms—AS MADARA COSMETICS, AS Latvijas Finieris, and AS Valmiera Glass Grupa—are compared with industry averages over the 2019–2023 period using independent sample T-tests. ESG integration is evaluated through a six-stage conceptual schema ranging from symbolic compliance to performance-driven sustainability. The results show that AS MADARA COSMETICS, which demonstrates advanced ESG integration aligned with international standards, significantly outperforms its industry in all profitability metrics. In contrast, the other two companies remain at earlier ESG maturity stages and show weaker financial performance, with sustainability disclosures limited to general statements and outdated indicators. These findings support the synergy hypothesis in contexts where sustainability is internalized and operationalized, while also highlighting structural constraints—such as resource scarcity and fragmented data—that may limit ESG-financial alignment in post-transition economies. This study offers practical guidance for firms seeking competitive advantage through strategic ESG integration and recommends policy actions to enhance ESG transparency and performance in Latvia, including performance-based reporting mandates, ESG data infrastructure, and regulatory alignment with EU directives. These insights contribute to the growing empirical literature on ESG effectiveness under constrained institutional and economic conditions. Full article