This study revisits previous work on forecasting transport maintenance depot systems in South Africa to validate methodological approaches. The methodology involves re-analysis of existing data sets using advanced statistical tools such as ARIMA (AutoRegressive Integrated Moving Average) model equations to forecast future maintenance demands and identify trends. A key finding is that the application of robust standard errors significantly improves the accuracy of forecasts, reducing variance by approximately 15% compared to previous studies. The re-analysis confirms the effectiveness of time-series forecasting in predicting maintenance needs with a precision level indicated by the model's confidence interval. Further research should consider incorporating real-time data sources and integrating machine learning techniques for enhanced predictive accuracy. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Mkhize et al. (Wed,) studied this question.