Time-Series Forecasting Model for Risk Reduction in Transport Maintenance Depots Systems: An Evaluation in Uganda

Transport maintenance depots (TMDs) in Uganda face challenges related to equipment reliability and maintenance efficiency. A time-series analysis was conducted using historical data from five selected TMDs. A SARIMA (Seasonal AutoRegressive Integrated Moving Average) model was applied to forecast future risks based on past performance metrics. The SARIMA model showed a significant reduction in prediction errors within the tested dataset, with an average error margin of ±5% for equipment failures over a two-year forecasting horizon. The time-series forecasting model demonstrated potential as a tool for risk management in TMDs, offering insights into future maintenance needs and resource allocation. Further studies should explore the scalability of this approach across different regions and incorporate real-time data sources to enhance accuracy. Transport Maintenance Depots, Risk Reduction, Time-Series Forecasting, SARIMA Model, Equipment Reliability The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.