Predictive Maintenance Algorithms, Artificial Intelligence Digital Twin Technologies, and Internet of Robotic Things in Big Data-Driven Industry 4.0 Manufacturing Systems

Marek Nagy, Marcel Figura, Katarina Valaskova and George Lăzăroiu ()
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Marek Nagy: Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
Marcel Figura: Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
Katarina Valaskova: Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
George Lăzăroiu: Faculty of Science and Engineering, Curtin University, Bentley, WA 6102, Australia

Mathematics, 2025, vol. 13, issue 6, 1-28

Abstract: 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.

Keywords: predictive maintenance; internet of robotic things; industry 4.0 manufacturing; artificial intelligence digital twin technologies (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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