@ARTICLE{Bednarek_Mariusz_Harnessing_2025,
 author={Bednarek, Mariusz and Luściński, Sławomir and Jabłoński, Marek and SCHAFFELD GRANIFFO, Guillermo Jorge},
 volume={No 1 - preprint},
 journal={Management and Production Engineering Review},
 howpublished={online},
 year={2025},
 publisher={Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management},
 abstract={A novel approach has emerged to enhance the efficiency and reliability of predictive maintenance strategies, namely the taxonomy approach for defining types of production machines. This innovative method represents a significant departure from traditional categorisation methods, promising to improve how organisations manage and maintain their production equipment. Organisations can reduce overall maintenance costs and minimise unplanned downtime through proactive maintenance based on taxonomy-driven insights, increasing operational efficiency and profitability. The article explores how the taxonomy approach leverages data analytics and machine learning techniques to classify production machines into distinct categories based on their operational characteristics, usage patterns, and maintenance needs. Doing so offers several key advantages: improved precision, predictive maintenance customisation, data-driven insights, and scalability. The taxonomy approach is based on data-driven insights, allowing organisations to harness the power of big data and the Industrial Internet of Things (IIoT). Maintenance teams can detect anomalies and issues by analysing real-time data from production machines before they lead to breakdowns. In the discussion part, a brief overview highlights the integration of predictive maintenance with Industry 4.0, the uniqueness of the proposed method, and its potential implications for modern production systems.},
 title={Harnessing Industry 4.0 Technologies: A Novel Predictive Maintenance Method for Advanced Production Systems},
 URL={http://ochroma.man.poznan.pl/Content/134189/PDF-MASTER/art_1083_318_MPER_1_2025_Bednarek.pdf},
 keywords={Industry 4.0, Predictive maintenance, Production Systems, Data analytics, IIoT},
}