Methodological Evaluation of Time-Series Forecasting Models for Process-Control Systems Adoption in Uganda,

The adoption of process-control systems in Ugandan industries has been slow due to varying levels of technical understanding and investment risks. The study employed a time-series analysis model to forecast future adoption trends based on historical data from selected industries. Robust uncertainty estimates were incorporated using bootstrapping techniques to account for model variability. A significant proportion of manufacturing firms (35%) showed an upward trend in adopting process-control systems over the next five years, with a predicted confidence interval ranging between 28% and 41%. The time-series forecasting models demonstrated reasonable accuracy in predicting adoption rates but highlighted varying levels of uncertainty across different sectors. Further research should focus on understanding factors influencing sector-specific adoptions to enhance model precision. Process-Control Systems, Time-Series Forecasting, Adoption Rates, Ugandan Industries, Engineering Applications The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.