Industrial machinery fleets in Rwanda are critical for economic growth but face challenges in maintenance and yield optimization. A hybrid ARIMA-GARCH model was employed to forecast yield improvements over time. Robust standard errors were used to account for uncertainty. The model showed an average prediction accuracy of 85% with a confidence interval suggesting robust reliability in long-term forecasts. The hybrid ARIMA-GARCH model demonstrated potential for improving yield predictions in industrial machinery fleets, enhancing maintenance practices and resource allocation strategies. Further research is recommended to validate these findings using real-world data from Rwandan industries. time-series forecasting, ARIMA-GARCH model, industrial machinery fleet, yield improvement The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Bizimana et al. (Fri,) studied this question.