October 5, 2025Open Access

Intelligent Automation and Digital Twins as a Tool for Optimizing Production Process Management

Key Points

Integrating digital twins significantly enhances production process efficiency and reduces costs.
The study demonstrates the ability of AI to optimize resource allocation and minimize equipment downtime.
A comparative analysis shows that digital management techniques outperform traditional methods in production settings.
The methodology presents a conceptual algorithm for implementing AI models in adaptive production systems under Industry 4.0.

Abstract

This paper examines a modern approach to production process automation through the application of Artificial Intelligence (AI) and digital twin technologies. The aim of the study is to identify the potential of intelligent automation to improve production system efficiency, reduce costs, and minimize equipment downtime. Using a comparative analysis of traditional and digital management methods, the author demonstrates how the integration of digital twins allows for failure prediction, resource load optimization, and real-time decision-making improvement. The paper proposes a conceptual algorithm for implementing AI models into production management systems and presents the results of pilot modeling that confirm the approach’s effectiveness. The practical significance of the study lies in establishing the basis for the development of adaptive, self-regulating management systems capable of ensuring sustainable productivity growth under Industry 4.0 conditions. Keywords: automation, digital twin, artificial intelligence, production management, optimization, modeling, Industry 4.0.

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