Transport maintenance depots (TMDs) play a crucial role in ensuring vehicle reliability and operational efficiency for various transportation sectors in Ethiopia. A comprehensive analysis was conducted to assess the performance of TMDs. The study utilised ARIMA (AutoRegressive Integrated Moving Average) model for forecasting vehicle downtime and predicting future system reliability. The ARIMA model predicted a reduction in average downtime by 15% over the next year, indicating improved maintenance scheduling strategies. The application of ARIMA models demonstrated significant potential for enhancing TMD systems' reliability in Ethiopia. Further research should focus on integrating machine learning algorithms to improve model accuracy and operational efficiency. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Abera et al. (Thu,) studied this question.
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