This study examines Industry 4.0-based technologies, focusing on the barriers to their implementation in European small- and medium-sized enterprises (SMEs). The purpose of this research was to determine the most significant obstacles that prevent SMEs from implementing smart manufacturing, as well as to identify the most important components of such an operationalization and to evaluate whether only large businesses have access to technological opportunities given the financial complexities of such an adoption. The study is premised on the notion that, in the setting of cyber-physical production systems, the gap between massive corporations and SMEs may result in significant disadvantages for the latter, leading to their market exclusion by the former. The research aim was achieved by secondary data analysis, where previously gathered data were assessed and analyzed. The need to investigate this topic originates from the fact that SMEs require more research than large corporations, which are typically the focus of mainstream debates. The findings validated Industry 4.0′s critical role in smart process planning provided by deep learning and virtual simulation algorithms, especially for industrial production. The research also discussed the connection options for SMEs as a means of enhancing business efficiency through machine intelligence and autonomous robotic technologies. The interaction between Industry 4.0 and the economic management of organizations is viewed in this study as a possible source of significant added value.

Research background: On the basis of an analysis of the current situation and expectations in the field of implementation of the elements of the Industry 4.0 concept, the purpose of this paper is to identify the effects on the labor market in large manufacturing enterprises in the Slovak Republic. Purpose of the article: The presented work has a theoretical-empirical nature and consists of a theoretical section and a practical section, which includes statistical indicator analysis and quantitative research. In the theoretical section, the paper discusses the issue of Industry 4.0 in general, with a focus on its impact on the labor market, thus laying the groundwork for future research on the subject. Methods: The output of this work is an analysis of selected indicators of the manufacturing industry sector in the Slovak Republic, based on the most recent employment data analysis in the first stage and quantitative research survey in the second stage, with the respondents being manufacturing industry companies operating in the Slovak Republic, and whose primary objective is to determine the current status of the implementation of the elements and technologies of Industry 4.0 in production companies in the Slovak Republic, as well as the factors influencing this situation, such as digital twin simulation modeling, artificial intelligence-based Internet of Manufacturing Things systems, and virtual machine and cognitive computing algorithms. Findings & value added: The research findings indicate that the degree of digitization adopted by businesses in the Slovak Republic is comparatively less robust and more sluggish to adapt. This is primarily attributable to the underdeveloped educational system, population reluctance, self-actualization, and inadequate state support. Recommendations for the Slovak market aim to increase the digital proficiency of businesses and of the general populace through various means, such as reforming legislation, enhancing state support for entrepreneurs, and modifying the education system, constituting the added value of the work.

Industry 4.0 integrates smart and connected production systems that are pivotal in predicting and supporting production in real-time, leading to sustainable organizational performance. In manufacturing, it may increase productivity, sustainability, and energy efficiency, while optimizing competitiveness. The main purpose of this paper is to determine the impact of Industry 4.0 on the Slovak economy through a secondary data analysis in the automotive industry, which is the leading sector in the country. The paper aims to provide a comprehensive analysis of the various opportunities that are available in the value-added growth of car exports in Slovakia. It also explores the case study of PSA Group Slovakia, which highlights the importance of the Industry 4.0 concept in boosting the country’s export growth. The paper proposes a series of recommendations and steps to improve Slovakia’s innovation environment.

The objectives of this paper, and the novelty brought to the topic of the Industry 4.0 manufacturing systems, are related to the integration of computer vision algorithms, remote sensing data fusion techniques, and mapping and navigation tools in the Slovak automotive sector. We conducted a thorough examination of Industry 4.0-based value and supply chains, clarifying how cyber-physical production systems operate in relation to collision avoidance technologies, environment mapping algorithms, and mobility simulation tools in network connectivity systems through vehicle navigation data. The Citroen C3 and Peugeot 208 automobiles are two examples of high-tech products whose worldwide value and supply chain development trends were examined in this study by determining countries and their contributions to production. The fundamental components of the research—statistical analysis and visual analysis—were utilized in conjunction with a variety of syntheses, comparisons, and analytical methodologies. A case study was developed using PSA Group SVK data. The graphical analysis revealed that Slovakia offers the second-highest added value to the chosen items, but it also highlighted the country’s slow-growing research and development (R&D) infrastructure, which could lead to a subsequent loss of investment and business as usual. Slovakia can generate better export added value by optimizing Industry 4.0-based manufacturing systems in the automotive sector.

Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and labor-intensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization. Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies.

In Industry 4.0, predictive maintenance (PdM) is key to optimising production processes. While its popularity among companies grows, most studies highlight theoretical benefits, with few providing empirical evidence on its economic impact. This study aims to fill this gap by quantifying the economic performance of manufacturing companies in the Visegrad Group countries through PdM algorithms. The purpose of our research is to assess whether these companies generate higher operational profits and lower sales costs. Using descriptive statistics, non-parametric tests, the Hodges–Lehmann median difference estimate, and linear regression, the authors analysed data of 1094 enterprises. Results show that PdM significantly improves economic performance, with variations based on geographic scope. Regression analysis confirmed PdM as an essential predictor of performance, even after considering factors like company size, legal structure, and geographic scope. Enterprises with more effective cost management and lower net sales were more likely to adopt PdM, as revealed by decision tree analysis. Our findings provide empirical evidence of the economic benefits of PdM algorithms and highlight their potential to enhance competitiveness, offering a valuable foundation for business managers to make informed investment decisions and encouraging further research in other industries.