Time-Series Forecasting Model for Evaluating Process-Control Systems in Ethiopian Industries: A Methodological Assessment

Recent industrial processes in Ethiopia have been characterized by varying degrees of efficiency due to diverse operational conditions. A time-series forecasting model was developed using historical data from industrial processes. The model incorporates robust standard errors for uncertainty assessment. The forecasting model indicated an average annual growth rate of 5% in process efficiency over a five-year period, with significant reductions in variability attributed to better PCS implementation. This study provides evidence that the time-series forecasting model can effectively evaluate and predict improvements in industrial processes in Ethiopia. The findings suggest enhanced investment in PCS for further efficiency gains in Ethiopian industries. Process-Control Systems, Time-Series Forecasting, Efficiency Gains, Industrial Processes, Ethiopia The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.