2025, issue 4, p. 106-114
Received 16.06.2025; Revised 03.08.2025; Accepted 18.11.2025
Published 08.12.2025; First Online 15.12.2025
https://doi.org/10.34229/2707-451X.25.4.10
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Industry 4.0. Overview of Trends, Challenges, and Solutions
Yevhenii Shcherbyna *
, Iryna Shpinareva
Odesa І.І. Mechnikov National University
* Correspondence: This email address is being protected from spambots. You need JavaScript enabled to view it.
Introduction. The development of science and technology has significantly changed approaches to enterprise organization, product manufacturing, and service provision. Currently, the world is transitioning to a new technological paradigm – Industry 4.0. This involves the use of IIoT systems, digital twins, artificial intelligence, cloud computing, edge computing, and data protection technologies. Integrating these technologies into an enterprise allows for the creation of a system that can handle tasks such as optimizing resource utilization, supporting decision-making, predictive maintenance, and improving product quality.
Purpose of the Work. The main goal of this article is to form a theoretical and methodological basis for analyzing the impact of Fourth Industrial Revolution technologies on enterprise automation processes, which is realized through the implementation of decision support systems and predictive maintenance of IoT systems. Specifically, this includes: studying the optimization of resource utilization and decision support in IoT systems; analyzing the main problems in solving these tasks; analyzing existing technologies and IT systems, as well as developing an IT system architecture that would have the capabilities to solve these problems and avoid identified issues.
Results. During the analysis of articles and existing market systems, it was found that the following problems exist in the implementation of Industry 4.0 technologies: outdated IT systems, equipment compatibility, cost and resource limitations, lack of personnel skills, change management, and cybersecurity issues. To solve the problems of decision support and resource optimization, key technologies were identified: digital twins, which allow obtaining up-to-date information about devices in IoT systems; spatio-temporal graphs and graph neural networks, configured to work with data that have certain patterns and interconnections; reinforcement learning, which allows training a model while interacting with the environment, enabling the model to adapt to changes in enterprise operations.
A microservice architecture for an IT system was developed to solve the aforementioned problems, integrating graph neural networks, reinforcement learning, digital twins, and an IoT system. Some of the main advantages of such an IT system are scalability, modularity, and adaptability to business needs.
Conclusions. This study demonstrates the relevance, trends, and effectiveness of implementing Industry 4.0 technologies in enterprises based on articles and existing software products. Challenges were identified, and key technologies to overcome them were determined. A microservice IT system architecture based on graph neural networks and digital twins was developed, which has prospects for solving resource utilization optimization and decision support tasks. Areas for additional research on the specific implementation of the proposed architecture's modules were identified, such as: dynamic adaptation to the environment with the combination of pre-known business rules; agent training with minimal performance degradation; digital twin model and data cleaning and transformation mechanisms.
Keywords: deep machine learning, reinforcement learning, IoT, IIoT, digital twins, Industry 4.0, graph neural networks.
Cite as: Shcherbyna Y., Shpinareva I. Industry 4.0. Overview of Trends, Challenges, and Solutions. Cybernetics and Computer Technologies. 2025. 4. P. 106–114. (in Ukrainian) https://doi.org/10.34229/2707-451X.25.4.10
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