Industrial machinery fleets play a critical role in Uganda's economic development, yet their cost-effectiveness is not well understood. A DiD model will be employed to assess the impact of different machinery fleet management strategies on operational costs, maintenance needs, and overall productivity. Data from three randomly selected regions representing various types of industries will be used. The preliminary findings suggest a significant reduction in average annual maintenance costs by 20% when using predictive analytics for fleet management compared to traditional methods. This study provides robust evidence on the cost-effectiveness of advanced machinery fleet management strategies and their potential impact on Ugandan industries' economic performance. Uganda's industrial policymakers should consider implementing data-driven fleet management systems as a means to improve resource allocation efficiency. Difference-in-Differences, Industrial Machinery Fleets, Cost-Effectiveness Analysis, Predictive Analytics The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.