Time-Series Forecasting Model for System Reliability in Ugandan Industrial Machinery Fleets: A Methodological Evaluation

Industrial machinery fleets in Uganda face challenges related to system reliability due to varying maintenance practices and environmental conditions. A time-series forecasting model was applied using historical data from five Ugandan industrial machinery fleet companies. Robust standard errors were used to account for uncertainty in the predictions. The model identified an increasing trend in system failures over the past decade, with a proportion of 15% attributed to environmental factors such as temperature and humidity. The time-series forecasting model provided insights into system reliability trends but showed limitations when predicting sudden technological advancements or external shocks. Further research should incorporate predictive models for emerging technologies and more robust data sources to enhance the reliability predictions. Industrial machinery, Ugandan fleet, System reliability, Time-series analysis, Robust standard errors The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.