Industrial machinery fleets in Uganda have seen significant growth over recent years, leading to increased operational efficiency and productivity. A time-series forecasting model was developed and applied to historical data from a representative sample of Ugandan industrial machinery fleets. The model's predictive accuracy was evaluated using robust standard errors. The time-series model forecasts showed an average efficiency gain of 12% in the predicted future fleet performance, with a confidence interval indicating ±3% uncertainty around these predictions. The developed forecasting model demonstrated potential for enhancing industrial machinery management and operational planning in Uganda. However, further research is needed to validate its applicability across different sectors. Investigate the scalability of this model across various industries within Uganda to ensure broad utility and adoption. Conduct pilot studies with diverse fleets to refine the model's predictive accuracy. Industrial machinery fleet efficiency, time-series forecasting, Ugandan industrial operations, robust standard errors The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Muhire et al. (2003) studied this question.