Manufacturing plants in Uganda are adopting new systems to enhance productivity and efficiency. However, understanding the adoption rates of these systems is challenging due to variability across different sectors and companies. A Bayesian hierarchical model was employed, incorporating prior knowledge about the sectors' characteristics. Data on system installation and usage were collected from 150 randomly selected plants across three major sectors: food processing, textiles, and mining. The analysis revealed significant variation in adoption rates between sectors (e.g., food processing had a higher adoption rate than textile and mining sectors). The Bayesian hierarchical model provided insights into the drivers of adoption and highlighted differences by sector, offering a nuanced understanding of system implementation. Policy makers should tailor their support strategies based on sector-specific challenges to facilitate broader uptake of manufacturing systems. Bayesian Hierarchical Model, Adoption Rates, Manufacturing Plants, Uganda The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.