{ "background": "The economic sustainability of power-distribution networks in sub-Saharan Africa is constrained by high capital costs and operational inefficiencies. Existing models for evaluating equipment cost-effectiveness often lack a robust, forward-looking component, limiting long-term infrastructure planning.", "purpose and objectives": "This paper presents a methodological evaluation of a novel time-series forecasting model designed to measure the cost-effectiveness of power-distribution equipment. The objective is to assess the model's predictive accuracy and utility for long-term capital planning.", "methodology": "The methodology employs an autoregressive integrated moving average with exogenous variables (ARIMAX) model, specified as $\\Delta yt = \\alpha + \\sum{i=1}^{p}\\phii \\Delta y{t-i} + \\sum{i=1}^{q}\\thetai \\epsilon{t-i} + \\sum{i=1}^{r}\\betai X{t-i} + \\epsilont$, where $yt$ is the cost-effectiveness ratio. Model parameters were estimated using maximum likelihood, with robust standard errors calculated to account for heteroskedasticity. Historical technical and financial data from a national utility were used for calibration and validation.", "findings": "The model demonstrates a statistically significant forecasting capability, with a mean absolute percentage error (MAPE) of 8.7% on the validation set. A key directional finding is that operational expenditure, rather than initial capital cost, is the dominant driver of long-term cost-ineffectiveness for transformers in the studied network. Forecast uncertainty, expressed via 95% prediction intervals, widens notably beyond a five-year horizon.", "conclusion": "The proposed ARIMAX framework provides a technically sound and operationally relevant method for forecasting the cost-effectiveness of distribution assets. It offers a material improvement over static, historical analysis for strategic asset management.", "recommendations": "Utilities should integrate such dynamic forecasting models into their asset management cycles. Further research should focus on incorporating climate resilience metrics as exogenous variables to enhance model robustness.",