*2.2. Decision Support Systems in Maintenance Management*

The problem of supporting decision processes in maintenance management has been the topic of many studies for over a dozen years now. Bashiri, Badri, and Hejazi [31] and Zhaoyang, Jianfeng, Zongzhi, Jianhu, and Weifeng [32] highlighted the role of risk-based maintenance in the maintenance management process. Cruz and Rincon point out that the maintenance process is at risk due to equipment failure [33]. Rinaldi, Portillo, Khalid, Henriques, Thies, Gato, and Johanning emphasized the importance of quantitative reliability, availability, and maintainability at early design stages [34]. Additionally, Wang, Furst, Cohen, Keil, Ridgway, and Stiefel predicted the risk of equipment failure using the Monte Carlo method [35], while in later works, they proposed approaches to monitoring disruptions and risk using ontologies and multi-agent systems [36].

Taghipour, Banjevic, and Jardine have proposed a method to identify and prioritize critical devices to mitigate functional failures [37]. Li, Parikh, He, Qian et al. incorporated machine learning techniques into the process of predicting failures, taking into account historical and real-time data analysis [38].

Jamshidi, Rahimi, Aitkadi, and Ruiz used fuzzy failure modes and analysis of effects to prioritize the operation of machinery, equipment, and classification [39], while Carnero and Gomez suggested the use of a multi-criteria model to increase the efficiency of the maintenance process [40].

Zeineb, Malek, Ahmand Ikram, and Faouzi, taking into account the total cost of ownership, used the Analytic Hierarchy Process (AHP) method to establish an appropriate-optimal maintenance program [41]. Moreover, fuzzy analytic hierarchy process for performing diagnostic and prescription tasks was discussed by Duran, Capaldo, and Duran Acevedo [27].

Lin, Yuan, and Tovilla use a continuous Markov chain model in a stochastic decision model that combines the effectiveness of maintenance activities and natural changes in state [42]. Jasiulewicz-Kaczmarek and Zywica use the non-additive fuzzy integral and balanced scorecard in the ˙ maintenance process [43]. In [44], the authors proposed a scorecard model that allows for monitoring the maintenance process in an enterprise. Finally, the importance of modern IT technologies in the maintenance decision-making process was also emphasized by Kosicka, Gola, and Pawlak [45].

Although the literature on the subject presents many solutions supporting decision-making in maintenance management, intelligent systems that are dedicated to supporting the implementation of the lean maintenance concept are not presented. For the time being, some limited results were presented by Antosz, Pasko, and Gola during the 13th IFAC Workshop on Intelligent Manufacturing Systems (Oshawa, ON, Canada) [46]. This explains why the research problem that was considered in the paper is an insufficient means of assessing the degree of the implementation of lean maintenance. This problem results in not only the possibility of achieving high efficiency of the exploited machines, but, foremost, it influences a decision process and the formulation of maintenance policy of an enterprise. In the context of the work conducted, the methodology of assessing lean maintenance was presented.

#### **3. The Work Methodology**

The research was carried out in two stages. The first stage of the study considered collecting the information on the systems of technical infrastructure management, in particular, the methods and tools of lean maintenance as well as the ability to identify the factors affecting the efficiency of their application. This stage in detail includes:


The detailed work methodology is presented on Figure 1.

**Figure 1.** The detailed work methodology of the research.

The studies were carried out in 150 manufacturing enterprises in Podkarpackie Voivodship (Poland). Qualitative as well as quantitative research methods were used to analyze the results obtained. Additionally, a statistical analysis with the chi square test of the obtained results allowed us to identify the factors that influence the lean maintenance implementation effectiveness. In the second phase of the study, the concept of using artificial intelligence (AI) methods was proposed in order to assess the effectiveness of the lean maintenance concept implementation.

Artificial intelligence methods were used to search for the relationship between specific activities carried out under the implementation of lean maintenance and the results obtained. Decision trees and the rough set theory were used for the analysis. Decision trees were made for the variable of the average value of the OEE indicator. Decision trees enabled the generation of decision rules that can be the basis for determining the directions and effects of implementing lean maintenance in manufacturing enterprises. The rough set theory was used in order to assess the degree of the lean maintenance concept usage.
