Digitalization-Based Predictive Maintenance Systems, Internet of Things Sensor Networks, and Machine Intelligence Algorithms for Industry 4.0/5.0 Manufacturing Processes

The digital transformation of manufacturing under Industry 4.0 has increased the relevance of predictive maintenance as a tool for improving operational efficiency and equipment reliability, particularly in the automotive industry. This study investigates how digitalization enables the integration of predictive maintenance in the Slovak automotive sector and evaluates its impact on firm performance throughout the analyzed time period, while accounting for heterogeneity in digital maturity. The analysis is based on panel data from 62 automotive manufacturing firms operating in Slovakia between 2018 and 2023, obtained from the ORBIS database. Non-parametric methods, including the Friedman test with Dunn’s post hoc comparisons, are applied to examine changes in selected financial indicators following predictive maintenance implementation. Robust regression analysis is used to assess the relationship between digital adoption and performance, controlling for firm size, and k-means cluster analysis is employed to classify firms according to digitalization and adoption levels. The results indicate statistically significant changes in several financial metrics, including sales, operating expenses, and balance sheet items, reflecting both the impact of the COVID-19 disruption and the importance of predictive maintenance. Higher levels of digital adoption are positively associated with sales performance, and clustering reveals clear differences between highly digitalized firms, moderate adopters, and lagging firms. The findings suggest that predictive maintenance supported by digitalization is associated with performance improvements and highlight remaining gaps in digital maturity relative to Western European benchmarks.