## 2 Materials and Methods  
### Survey Instrument and Data  
The questionnaire-based survey instrument has been developed to target manufacturing companies in the West Balkan region. The survey consists of eighteen predictor variables, four meta-data records, and five target variables (Figure 1). The survey's response rate is 38.72% (115/297). The extracted variables were subjected to coding. Specifically, NWEC (Nominal Working Energy Consumption) is determined based on the NWP (Nominal Working Pressure) and NWF (Nominal Working Flow) metrics, expressed in kW power.  
Similarly, the Maintenance Personnel Per Machine (MPPM) was determined based on the number of Maintenance Personnel (nMANP) and Number of Machines (NOM) utilising fluid power systems. The rest of the survey data are taken as raw data. The full description and design of the survey instrument is provided in detail [11]. However, unlike in the previous study, we specifically aim to assess the impact of individual predictors on the MTTR, MTBF, and WOM metrics. Thus, RCF (Root Cause of Failure) and CFT (Component Failure Type) variables are not included in this study.  
Moreover, additional coding of variables is performed. For FAP (Failure Analysis Personnel), the data are split into indoor and outdoor (outsource) Failure Analysis Personnel because we consider it would increase the generalisability of findings that would be more of an interest to the readers. Also, the LCML (Lubricant Condition Monitoring Laboratory) variable is coded as a binary variable as 1 = includes laboratory analysis (e.g., oil spectrophotometry) and 0 = does not include laboratory analysis. The LCMI (Lubricant Condition Monitoring Instruments) variable is also coded as binary, such that 1 = includes advanced condition monitoring instruments (e.g., contamination sensors and vibroacoustic sensors), and 0 = does not include. Lastly, the DAT (Data Analytics Tools) utilised variable is coded as 1 = includes data analysis tools and 0 = does not include data analysis tools.  
The rest of the categorical and continuous predictors are explained and discussed through hypothesis testing, including the impact of each category and the association between continuous and target variables. Lastly, MTTR, MTBF, and WOM's output performance metrics were subjected to dimensionality reduction. Specifically, we use PCA (Principal Component Analysis) and select the first PC (Principal Component), which is commonly used since it captures the most variance. Next, we perform a regression analysis of extracted components using coded categorical and continuous variables to capture the contribution of each feature in the PC1 using the MDL algorithm. The results of dimensionality reduction using PCA are provided below.  
The PCA is a commonly known unsupervised method used for dimensionality reduction and visualisation. [18]. Namely, the PCA transforms the original dataset into a new set  
Figure 1: The questionnaire-based survey description of input and output variables and estimated parameters (reworked [17]).  
of variables through ordination, commonly known as Principal Components [19]. These PCs are then ordered by the variation they capture from the original dataset. The reader is referred to for an in-detail analytical description of the PCA [20; 21]. The dimensionality reduction results using PCA show the loadings (Table 1) of individual components and PC coefficients (Table 2). The variance for the first two components accounts for 79.7% of the total variance, while the projected first component accounts for 48.8% of the total variance. The PC1 is then used to perform an ML regression analysis using the standardised raw data, after which the contribution of individual predictors is determined by the MDL. The ML regression models are explained in the following.