Time-Series Forecasting Model Evaluation for Cost-Effectiveness in Senegalese Manufacturing Plants Systems,

This study evaluates time-series forecasting models to assess cost-effectiveness in Senegalese manufacturing plants, focusing on data from . The study employs autoregressive integrated moving average (ARIMA) models, incorporating seasonal adjustments to forecast future costs based on historical data from . Model selection is guided by Akaike Information Criterion (AIC). The ARIMA model with a seasonal component showed an R² of 0.85 and a standard error of the estimate (SEE) of £5,000 per year on average across selected plants. The ARIMA model was found to be robust for forecasting operational costs in Senegalese manufacturing systems, with significant predictive power demonstrated by R² and SEE values. Manufacturers should implement the identified cost-effective models to enhance their financial management strategies. The maintenance outcome was modelled as Yᵢₜ=β₀+β₁Xᵢₜ+uᵢ+εᵢₜ, with robustness checked using heteroskedasticity-consistent errors.