African Computational Statistics (Technology/Maths) | 18 August 2006
Bayesian Hierarchical Model for Measuring Cost-Effectiveness of Process-Control Systems in Uganda
N, a, k, a, m, w, e, M, w, e, s, i, g, a, ,, K, i, z, z, a, O, k, u, r, u, t
Abstract
The cost-effectiveness of process-control systems (PCs) in manufacturing environments is a critical issue for industries aiming to optimise resource utilization and reduce operational costs. A Bayesian hierarchical regression model was employed to analyse data from multiple Ugandan manufacturing sites. The model accounts for both site-specific and shared effects among processes. The analysis revealed that the cost-effectiveness of PCs varied significantly across different factories, with some showing a reduction in costs up to 30% compared to conventional control methods. This study provides evidence supporting the use of Bayesian hierarchical models for assessing process-control systems' economic impacts in Ugandan settings. The findings suggest that localized implementation and continuous monitoring are necessary for realising full cost-effectiveness benefits from PCs. Process-Control Systems, Cost-Effectiveness Analysis, Bayesian Hierarchical Model, Manufacturing Industries, Uganda The maintenance outcome was modelled as $Y<em>{it}=\beta</em>0+\beta<em>1X</em>{it}+u<em>i+\varepsilon</em>{it}$, with robustness checked using heteroskedasticity-consistent errors.