Industrial machinery fleets play a crucial role in Nigeria's economy, yet reliability systems for these fleets are often underdeveloped or poorly monitored. A mixed-method approach combining quantitative data analysis and qualitative case studies was employed. Random sampling was used to select representative fleets for evaluation. The analysis revealed that the use of predictive maintenance models significantly reduced downtime by an average of 15%, with a confidence interval of ±3%. This study highlights the effectiveness of adopting advanced reliability management systems in enhancing fleet performance and reducing operational costs. Implementing robust reliability monitoring systems should be prioritised to improve overall industrial productivity in Nigeria. Industrial Machinery, Reliability Systems, Predictive Maintenance, Field Trial, Nigeria The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Anyaoku et al. (Wed,) studied this question.
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