Application of Pls-Sem in Developing a Maintenance Management Model to Enhance Generator Maintainability in Government Institutions
Abstract
This study applies Partial Least Squares Structural Equation Modelling to develop a maintenance management model aimed at enhancing the maintainability of government-owned generators. Key factors influencing maintainability, such as equipment age and condition, operational environment, regulatory compliance, maintenance practices, design complexity, spare parts availability, electrical connection integrity, fuel quality, load management, and personnel competence, were analysed. Data from 68 generators, including downtime, failure frequency, and Mean Time to Repair (MTTR), were normalised and weighted to quantify factor importance. Total Downtime (weight = 0.65) was identified as the most critical performance metric, followed by Frequency of Failures (0.26) and MTTR (0.14). PLS-SEM path analysis revealed that Operational Environment (0.838) and Electrical Connection Integrity (0.584) positively impact repair efficiency, whereas poor Load Management & Generator Sizing (-1.264), advanced generator age (-0.645), and high Design Complexity (-0.593) negatively influence maintainability. The refined regression model shows that Avg. MTTR serves as a key mediating variable linking operational and design factors to maintainability outcomes. Model validation using R² (0.763), CR (≥ 0.70), AVE (≥ 0.50), and fit indices (RMSEA < 0.08, CFI/TLI ≥ 0.90) confirmed the model’s predictive and explanatory power. The proposed framework (Figure 1) provides a structured, evidence-based approach for prioritising maintenance interventions, optimising resource allocation, and improving generator reliability, availability, and operational efficiency in government institutions.
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Copyright (c) 2025 Jefta Dickson Moshi, Victor Pius Chombo, PhD
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