## 1 Introduction  
Overall Equipment Effectiveness (OEE) is no longer solely a target of manufacturing companies. Many companies are altering their business models and incorporating sustainability aspects in their performance metrics [1; 2; 3; 4]. Specifically, in asset-intensive industries where processes and production heavily rely on fluid power systems, ensuring the availability and sustainability of these assets is crucial. Recent advancements in digital technology offer new opportunities for advancing maintenance practices. As such, an exponential rise in publications dedicated to Predictive Maintenance (PdM), Energy-Based Maintenance (EBM), and Sustainable Maintenance (SM) has been reported [5; 6; 7; 8; 9]. However, adopting these advanced maintenance practices varies by company and is questionable in terms of suitability and readiness. This is especially important due to the heterogeneity of applications relying on fluid power systems and other factors, such as the availability of skilled personnel [10]. Due to limited knowledge and lack of empirical evidence, the study aims to investigate critical factors affecting the operational and sustainability performance of companies utilising fluid power systems. To achieve this, we rely on evidence from a questionnaire-based survey [11] of companies in the West Balkan region. The idea is to raise awareness and understand how specific maintenance activities, tools, and organisational characteristics affect the performance metrics expressed in Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and Waste Oil Monthly (WOM). There is a lack of knowledge in contemporary maintenance research regarding the factors impacting the maintenance performance metrics [12]. With the emergence of SM, many studies engage in observational and explanatory research relying on survey data. For instance, a study by [13] investigates the impact of traditional preventive maintenance versus EBM practices, suggesting that although EBM may offer benefits in reducing energy consumption and environmental impact, it significantly reduces operational effectiveness in terms of MTBF, even up to 53% drop due to lack of competences and skills in EBM domain. A recent study by [PERSON] et al. [14] using SEM (Structural Equation Modelling) highlighted the impact of business analytics and big data on predictive maintenance and asset management practices, suggesting a significant positive relationship with the use of data analytic tools. The findings suggest significant improvements in asset reliability, reduced downtime, and enhanced operational efficiency. From an operational perspective, the role of management commitment and TPM (Total Productive Maintenance) implementation strategies suggest a significant benefit to companies' productivity, as reported by [PERSON] et al. [15]. They conclude that managerial commitment, as a latent variable, accounts for 40.7% of the variation, implying that for a maintenance program to be successful, managerial support is crucial for increasing maintenance performance. Their findings demonstrate with a moderate to large effect size that management commitment is vital in productivity, TPM success, and PM programs. Lastly, considering the contextual settings regarding the impact of factors on companies relying on fluid power systems, a study conducted by [PERSON] et al. [16] using a regression model suggests that filter replacement time, machine age, Failure Analysis Personnel, and maintenance practice significantly impact MTBF (\(p<0.01\)). The study continues previous work [11] to assess the factors influencing the maintenance performance of assets using fluid power systems. However, instead of investigating the factors leading to specific component failures, failure modes, and root causes of failure, this study relies on quantitative analysis using the Model-Agnostic Interpretation (MAI) method [17] of utilised Machine Learning (ML) models.