Industrial machinery fleets play a crucial role in Rwanda's manufacturing sector, yet their operational efficiency is not well understood. A mixed-method approach combining survey data collection with econometric analysis was employed. The study utilised the Difference-in-Differences (DiD) model for causal inference. The DiD regression revealed a significant increase of 15% in machinery utilization efficiency post-policy intervention, with robust standard errors indicating high confidence in these results. This quasi-experimental design provides a robust framework for evaluating industrial machinery fleet efficiency gains and can inform policy-making to enhance manufacturing productivity. Policy recommendations include promoting regular maintenance schedules and investing in advanced technologies to further improve efficiency. Rwanda, Industrial Machinery Fleets, Quasi-Experimental Design, Efficiency Gains, Difference-in-Differences (DiD) The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.