This study focuses on evaluating municipal infrastructure asset systems in Ethiopia by applying time-series forecasting models to measure efficiency gains. A systematic approach was adopted, including data collection from municipal records and public databases, application of ARIMA (AutoRegressive Integrated Moving Average) model to predict future maintenance needs based on historical performance data, and inclusion of robust standard errors for uncertainty quantification. The analysis revealed a significant correlation between the number of reported asset failures and predicted demand, with an R-squared value of 0. 75 indicating substantial explanatory power of the ARIMA model. This study validates the effectiveness of time-series forecasting in enhancing municipal infrastructure management efficiency in Ethiopia. The findings suggest implementing a predictive maintenance strategy to align with forecasted asset demands, thereby optimising resource allocation and reducing unexpected expenses. Municipal Infrastructure, Time-Series Forecasting, ARIMA Model, Asset Management, Efficiency Gains The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Kassa et al. (Fri,) studied this question.