Manufacturing plants in Ethiopia are facing challenges related to system reliability due to varying operational conditions and resource constraints. A hybrid ARIMA-GARCH (AutoRegressive Integrated Moving Average-Generalized Autoregressive Conditional Heteroskedasticity) model was employed to forecast time-series data from Ethiopian manufacturing plants. The methodology involved collecting and analysing historical operational data, feature engineering, model selection based on AIC and BIC criteria, and validation through cross-validation techniques. The ARIMA-GARCH model demonstrated an accuracy of over 85% in predicting system failures within a one-year forecast horizon, with the mean absolute error (MAE) being 2. 3%, indicating a significant improvement in reliability assessment compared to previous methods. This study provides empirical evidence supporting the use of ARIMA-GARCH for time-series forecasting in manufacturing systems, offering a practical tool for enhancing system reliability and operational efficiency in Ethiopian plants. Manufacturing plant managers are encouraged to adopt this methodology for proactive maintenance planning, leading to reduced downtime and increased productivity. Additionally, further research should be conducted to validate the model across different types of manufacturing environments. ARIMA-GARCH, time-series forecasting, system reliability, Ethiopian manufacturing plants The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Gobena et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: