Industrial machinery fleets in Uganda face significant operational risks that can lead to equipment downtime and increased maintenance costs. A mixed-method approach was employed, including surveys, interviews, and data analysis using statistical models to assess system performance and identify areas for improvement. The randomized field trial demonstrated that the adoption of a predictive maintenance model significantly reduced equipment downtime by an average of 15% compared to traditional fleet management practices. The study concluded that implementing advanced fleet management systems can lead to substantial reductions in risk and operational costs for industrial machinery operations in Uganda. Ugandan industries are encouraged to adopt predictive maintenance models as a standard practice, alongside ongoing training and technological upgrades. Industrial Machinery Fleet Management, Predictive Maintenance, Risk Reduction, Randomized Field Trial The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.