<![CDATA[Heavy equipment performance is an important factor that can affect productivity factors in the Indonesian coal mining sector. A major reason behind the high downtime is because they follow time-based maintenance (TBM) methods on their heavy equipment. This has led to the search of new and much better and effective methods such as Condition-Based Maintenance (CBM) method. The use of CBM as a predictive maintenance methodology by coal industry players to minimise equipment downtime is proliferating but, at present, little evidence exists regarding how operational environmental conditions and human resource competencies impact the effectiveness of CBM. This study investigates the impact of Predictive Data Analysis (PDA), Maintenance Actions Proactive (MAP) and Supporting Technology (STE) on heavy equipment performance (HEP) and which include Operational Environment Conditions (OEC) and Human Resource Competency (HRC) as moderating variables. This study collected data from 207 operational and maintenance personnel in Indonesian coal mining companies that have adopted CBM. Analysis of data was conducted through Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart-PLS 4.0 software. The results showed that PDA, MAP, and STE were positively related to heavy equipment performance, with MAP showing the strongest relationship among the three CBM dimensions. OEC and HRC also had a significant direct effect on performance. However, among the proposed moderating relationships, only the interaction between HRC and MAP was statistically significant. These findings suggest that CBM effectiveness is influenced not only by maintenance-related practices but also by workforce capabilities. This study provides empirical evidence on the relationship between CBM implementation, operational conditions, human resource competencies, and heavy equipment performance in the context of coal mining operations.]]>
