Industrial machinery fleets play a crucial role in enhancing productivity and efficiency in manufacturing industries across Tanzania. The study employed a mixed-method approach including survey data collection and statistical analysis to assess fleet performance metrics under varying operational conditions. A multivariate regression model was used to estimate the cost-effectiveness of machinery fleets across different sectors. A significant proportion (72%) of surveyed companies reported improved productivity with an average return on investment (ROI) of $50,000 per year for implementing advanced fleet management systems. The quasi-experimental design provided robust insights into the cost-effectiveness of machinery fleets in Tanzanian manufacturing, highlighting best practices and areas for improvement. Manufacturing companies are advised to adopt advanced fleet management solutions to achieve higher ROI and improve overall operational efficiency. Quasi-Experimental Design, Industrial Machinery Fleets, Cost-Effectiveness, Tanzania The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.