Time-Series Forecasting Model for Evaluating Cost-Effectiveness of Industrial Machinery Fleets in Nigeria

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 $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.