Methodological Evaluation of Transport Maintenance Depot Systems in Rwanda Using Time-Series Forecasting for System Reliability Assessment

Transport maintenance depots (TMDs) play a crucial role in ensuring efficient vehicle operation within Rwanda's logistics sector. A novel approach combining ARIMA (AutoRegressive Integrated Moving Average) model with robust standard errors was employed for system reliability assessment in Rwanda's transport maintenance depots. The ARIMA model demonstrated a correlation coefficient of 0.95 between actual and forecasted vehicle downtime, indicating high predictive accuracy. This methodological evaluation provides a robust framework for assessing and improving the reliability of TMD systems in Rwanda. Implementing continuous monitoring and maintenance schedules based on ARIMA forecasts can significantly reduce vehicle downtime and enhance fleet efficiency. Transport Maintenance Depots, Time-Series Forecasting, System Reliability, ARIMA Model The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.