Industrial machinery fleets play a crucial role in enhancing productivity across various sectors in Kenya. However, the efficiency and impact of these fleets on yield improvement have not been systematically evaluated. The study employs a fixed effects model (FE) to analyse the impact of various machine types, usage frequency, and maintenance practices on yield outcomes across different industries in Kenya. Panel data from five years of industrial machinery records were used to ensure robustness and reliability of findings. A significant proportion (75%) of fleet operations showed a positive correlation with higher yields, indicating that proper management and utilization can substantially boost productivity. However, maintenance lapses accounted for 20% of the variance in yield outcomes, suggesting areas requiring focused intervention. The panel-data estimation approach provides valuable insights into the dynamics of industrial machinery fleets and their impact on yield improvement in Kenya. The findings highlight the importance of consistent maintenance and optimised fleet management strategies. Based on the study's results, it is recommended that policymakers implement targeted maintenance programmes and provide training for operators to ensure sustainable productivity gains across all sectors. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Muthambi et al. (Fri,) studied this question.
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