African Construction Management and Engineering (Engineering focus) | 19 October 2000
Time-Series Forecasting Model for Risk Reduction in Transport Maintenance Depots Systems: An Evaluation in Uganda
G, r, a, c, e, N, a, k, a, w, u, k, i, ,, J, a, n, e, N, a, m, a, y, a, n, j, a, ,, S, a, m, u, e, l, O, k, u, m, u, ,, P, a, t, r, i, c, k, K, i, z, z, a
Abstract
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 $Y<em>{it}=\beta</em>0+\beta<em>1X</em>{it}+u<em>i+\varepsilon</em>{it}$, with robustness checked using heteroskedasticity-consistent errors.