Industrial machinery fleets play a critical role in Nigeria's agricultural sector, influencing productivity and profitability. However, limited studies have evaluated their cost-effectiveness over time. The research employs an autoregressive integrated moving average (ARIMA) model to forecast future costs based on historical data from. Robust standard errors are used to account for forecasting uncertainty. A trend analysis revealed that machinery operational hours increased by 15% annually over the study period, indicating growing demand and efficiency improvements. The ARIMA model successfully predicted cost trends with a coefficient of determination (R²) of 0. 82, highlighting its effectiveness in assessing fleet cost-effectiveness. Policymakers should consider subsidies for maintenance to reduce long-term costs while promoting technological upgrades to enhance efficiency and sustainability. Industrial machinery fleets, Nigeria, Cost-effectiveness, Time-series forecasting, Autoregressive integrated moving average (ARIMA) The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Olumide et al. (2003) studied this question.