Industrial machinery fleets play a crucial role in Ghana's manufacturing sector, influencing productivity and economic growth. However, little systematic analysis has been conducted to assess their operational efficiency. The methodology employed a stochastic frontier analysis (SFA) approach, utilising panel data from to across multiple industrial sectors in Ghana. Robust standard errors were applied to account for potential heteroscedasticity and autocorrelation within the dataset. A clear direction of efficiency gains was observed with significant improvements in operational efficiency, particularly among machinery fleets that utilised more advanced maintenance protocols. The study underscores the importance of adopting systematic performance evaluation methods to enhance industrial machinery fleet operations in Ghana. The findings contribute to a better understanding of sector-specific inefficiencies and provide actionable insights for stakeholders. Stakeholders are encouraged to implement predictive maintenance strategies, optimise resource allocation, and invest in employee training programmes to drive further efficiency improvements. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Asareduojo et al. (Wed,) studied this question.