Purpose – The purpose of this paper is to analyse the links between physical asset management policy and strategy, life cycle management of physical assets and operational performance. Even though there is some empirical evidence that physical asset management practices associate with performance outcomes of an organization, there is a need to further explore the interrelationship between physical asset management practices and organizational performance outcomes.Methodology/Approach – The data used in this study were obtained from a research project conducted by a team of international researchers in the field of maintenance and asset management. The target survey population consisted of international e-mail lists of managers across a wide range of functions. In total, 138 usable responses were collected during the given time window. The questionnaire was responded by organizations that are located in located in Slovenia, Poland, Greece, Sweden, Turkey and Slovakia, in portion of 31.9%, 34.1%, 16.7%, 6.5%, 5.8% and 5.1%, respectively. In terms of organizational size (following the guidelines of the Statistical Office of the Republic of Slovenia), 12.2% of the sample was composed of micro-enterprises having five or fewer employees, 17.4% were small-sized organizations employing 50 or less employees, 31.3% were medium sized organizations, employing 51–250 employees, 21.7% organizations were with 251–500 employees and 12.2% organizations were with more than 500 employees. Based upon Slovenian Standard Industrial Classification Codes (SIC), most respondents (39.3%) indicated that their organization is in the manufacturing industry.We applied the Partial Least Squares Path Modeling (PLS-PM) using the R package plspm to assess measurement and structural model (Sanchez, 2013). The method is suitable for studying complex multivariate relationships among observed and latent variables. PLS-PM uses an iterative algorithm that firstly provides weights, loadings and other relevant statistics for estimation of the blocks of the measurement model. Subsequently, PLS-PM algorithm estimates the path coefficients in the structural model (Esposito Vinzi et al., 2010).Findings – Within the scope of PLS-PM measurement model (outer model) assessment, loadings and communalities were checked. As suggested by Sanchez (2013) loadings should be above the value of 0.7. The outer model assessment results (loadings, weights and communalities) for studied constructs are presented in Appendix 1 and 2. According to the results, characteristics of measurement model appears to be suitable for further analysis of PLS-PM. There were a few indicators with loadings just below the recommended value of 0.7, however, they were kept in the measurement model due to content considerations.