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

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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
    Intelligent Communication and Computing Laboratory, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
    The Creative Computing Research Centre, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
    Department of Economic Sciences, Spiru Haret University, 030045 Bucharest, Romania)

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.

Suggested Citation

  • Marek Nagy & Marcel Figura & Katarina Valaskova & George Lăzăroiu, 2025. "Predictive Maintenance Algorithms, Artificial Intelligence Digital Twin Technologies, and Internet of Robotic Things in Big Data-Driven Industry 4.0 Manufacturing Systems," Mathematics, MDPI, vol. 13(6), pages 1-28, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:981-:d:1613984
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    References listed on IDEAS

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