The reliability of manufacturing plant systems in Uganda is a critical issue affecting productivity and economic growth. Current methods often rely on traditional statistical techniques which may not fully capture system complexities. The study employs a quasi-experimental design with appropriate control and treatment groups to measure system reliability. Key variables include operational efficiency, maintenance costs, and downtime frequency. A preliminary analysis suggests that the proportion of manufacturing plants experiencing frequent downtime is significantly higher than expected under normal operating conditions. The findings indicate a need for more sophisticated methodological approaches to accurately assess system reliability in Ugandan manufacturing environments. Future research should consider integrating machine learning algorithms into quasi-experimental designs to enhance the predictive accuracy of system reliability assessments. Manufacturing Systems, Reliability Assessment, Quasi-Experimental Design, Machine Learning The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Ssekitarama et al. (Fri,) studied this question.