IFAC PapersOnLine 58-8 (2024) 133–138 ScienceDirect ScienceDirect Available online at www.sciencedirect.com 2405-8963 Copyright © 2024 The Authors. This is an open access article under the CC BY-NC-ND license. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2024.08.062 analysis of historical data increases the effectiveness of decisions made and thus reduces losses caused by machine downtime. However, most often this analysis is based on the experience of the decision-maker, and a formal approach based on mathematical methods and models is used to a very limited extent. Although experience is important and influences the effectiveness of decisions, using data analysis methods we can significantly increase this effectiveness. The large amount of historical data contains information about events that occur along an industrial production line. Having a set of historical data about emergency events and their causes makes it possible to automate decision-making processes based on a data-driven approach. Data-driven approaches, particularly machine learning (ML), are attracting attention. According to Quatrini et al. (2020) “by using ML tools it is possible to discover the relationship between different factors and analyze the degree of influence of related variables”. By training models on historical data, these approaches enable more targeted maintenance activities (Çınar, et al., 2020; Cline, et al., 2017). The novelty of the study lies in the use of relatively simple, fast, and computationally inexpensive ML methods (from the group of decision trees and random forests) to analyze complex maintenance problems. This approach will facilitate both the development of a family of scalable ML systems for use in predictive maintenance in companies of all sizes, including those without large maintenance department structures and their implementation on mobile devices (smartphones, tablets), which will be handier to use in industrial settings (including the Industrial Internet of Things). 1. INTRODUCTION In today's competitive landscape, maintenance has become one of the most important business functions, and having an effective maintenance system is important to ensure production continuity, quality, and timely delivery to the customer, reduce losses, and ensure operational safety Over the years, maintenance management professionals have implemented many diverse types of maintenance strategies to make their maintenance programs as effective, efficient, and responsive as possible. The goal of the latest evolution of this task, predictive maintenance (PdM), is to monitor the condition of machines to decide when repair/replacement is necessary, taking into account the degree of degradation (Wang, et al., 2023; Saihi, et al., 2023). This approach is becoming more and more popular among both scientists and practitioners. However, in many enterprises, maintenance activities are still planned and implemented based on manufacturer recommendations and/or historical data regarding failure events, maintenance activities, and their effectiveness. Despite increasingly effective methods of planning maintenance activities, machine and equipment failures still occur, and the maintenance staff, based on the failure report, must decide as quickly as possible what actions to take and what human and material resources to allocate to ensure that the downtime caused by the failure is as short as possible. To determine the scope of work and the resources needed in many enterprises, maintenance employees use data on similar events that occurred on this machine or similar machines. The Keywords: data-driven maintenance, decision-making, machine learning, decision tree, boosted trees Abstract: The large amount of historical data contains information about events that occur along an industrial production line. Having a set of historical data about emergency events and their causes makes it possible to automate decision-making processes based on a data-driven approach. Data-driven approaches, particularly machine learning (ML), are attracting attention. Due to its visualization and interpretability characteristics, the decision tree (DT) model is an important ML tool for decision analysis. This paper aims to present the possibility of using DT to increase the efficiency and effectiveness of maintenance activities by identifying the probable cause of failure based on historical data. Based on the research conducted, we have shown that the use of machine learning techniques can improve the accuracy of decisions regarding the type of maintenance work that should be carried out to efficiently and effectively remove failures and reduce losses caused by machine downtime. Keywords: data-driven maintenance, decision-making, machine learning, decision tree, boosted trees *Faculty of Computer Science, Kazimierz Wielki University, Bydgoszcz, 85-064 Poland(e-mail: izabela.rojek@ukw.edu.pl, dariusz.mikolajewski@ukw.edu.pl) ** Faculty of Engineering Management, Poznan University of Technology in Poznan, 60-965 Poland (e-mail:malgorzata.jasiulewicz-kaczmarek@put.poznan.pl) *** Faculty of Engineering Management, WSB Merito Univeristy of Poznań, 61-895 Poland (e-mail: mariusz.piechowski@wsb.poznan.pl) Izabela Rojek*Małgorzata Jasiulewicz-Kaczmarek** Mariusz Piechowski*** Dariusz Mikołajewski* The use of decision trees to identify the causes of failures in a medical enterprise - a case study Copyright © 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)