{ "background": "The reliability of power-distribution networks is critical for national infrastructure. In South Africa, ageing equipment and operational challenges have heightened the risk of systemic failures, necessitating a robust, data-driven methodology for evaluating equipment performance and prioritising interventions.", "purpose and objectives": "This case study aims to develop and apply a panel-data estimation model to methodologically evaluate the performance of key power-distribution equipment systems. The primary objective is to quantify risk reduction associated with targeted maintenance and replacement strategies.", "methodology": "A balanced panel dataset was constructed from utility records for three major equipment classes across multiple municipalities. The core analysis employs a fixed-effects model: $Risk{it} = \\alphai + \\beta1 Age{it} + \\beta2 Maintenance{it} + \\beta3 Load{it} + \\epsilon{it}$, where $\\alphai$ captures entity-specific effects. Inference is based on robust standard errors clustered at the municipal level.", "findings": "The model indicates a statistically significant non-linear relationship between equipment age and failure risk. A key concrete result is that a strategic maintenance programme targeting assets in the 15–25 year age bracket was associated with a projected 18% reduction in the annualised risk index. The coefficient for preventive maintenance was negative and significant at the 1% level.", "conclusion": "The panel-data approach provides a rigorous methodological framework for moving from reactive to predictive asset management. It confirms that data-led prioritisation, rather than blanket replacement, is effective for systemic risk reduction.", "recommendations": "Utilities should adopt panel-data models for ongoing asset health monitoring. Investment should be prioritised based on the identified risk-age profile, focusing on enhanced condition assessment for the critical age cohort highlighted in the findings.", "key words": "asset management, distribution networks, failure risk, fixed-effects model, infrastructure reliability, panel data, predictive maintenance", "contribution statement": "This study provides a novel application of panel-data econometrics to power