Automation of Production Management Processes Using Artificial Intelligence: Impact on the Efficiency and Resilience of Manufacturing Systems

Kateryna Kolos, Oleg Kubrak, Yuliya Olimpiyeva, Pavlo Ihnatenko and Olena Furtat

LatIA, 2025, vol. 3, 311

Abstract: The rapid technological advancement and global competition provokes the automation of production management processes through artificial intelligence. This study investigates the integration of artificial intelligence into production management and its influence on the efficiency and resilience of manufacturing systems. The research is motivated by the growing relevance of AI within the paradigm of Industry 4.0, where advanced digital technologies are transforming traditional production models. The main objective is assessing how AI technologies – such as machine learning, deep learning, predictive analytics, and intelligent automation – enhance core production functions, including planning, quality control, maintenance, logistics, and energy management. The study applies a mixed-method approach, combining comparative analysis, case study evaluation, and content analysis of scientific and industrial data. Empirical evidence (1653 records) was drawn from both international (e.g., Siemens, Fanuc, Bosch) and Ukrainian (e.g., Interpipe, Kernel) manufacturing companies. Results after screening, filtration, validation, verification and exclusion (50 records) demonstrate measurable improvements in key performance indicators, such as reduced downtime, decreased defect rates, increased logistical accuracy, and optimized energy use. At the same time, the paper addresses the challenges accompanying AI integration, including cybersecurity risks, social impacts, regulatory gaps, and organizational readiness. The research concludes that AI not only improves operational performance but also strengthens adaptive capacity and strategic stability, contributing to the formation of intelligent, self-learning, and data-driven production systems. This article will be of particular interest to production managers, industrial engineers, innovation strategists, policymakers, and academic researchers seeking to understand and apply AI for sustainable industrial transformation.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:rlatia:v:3:y:2025:i::p:311:id:1062486latia2025311

DOI: 10.62486/latia2025311

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