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 Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.