What question did this study set out to answer?

Evaluate the cost-effectiveness in Senegal's manufacturing sector using time-series forecasting models.

February 23, 2026Open Access

Methodological Evaluation of Manufacturing Plant Systems in Senegal Using Time-Series Forecasting Models for Cost-Effectiveness Assessment

Puntos clave

Evaluate the cost-effectiveness in Senegal's manufacturing sector using time-series forecasting models.
Employ ARIMA model for cost forecasting
Analyze data from five manufacturing sectors
Quantify uncertainty with bootstrapping techniques
ARIMA model achieved an R² of 0.87
Mean absolute error within an acceptable range
Forecasting indicates ±3% uncertainty for cost projections

Resumen

Manufacturing plants in Senegal are pivotal for economic growth but face challenges in cost-effectiveness. A comprehensive analysis employing ARIMA (AutoRegressive Integrated Moving Average) model for forecasting costs, with uncertainty quantified through bootstrapping techniques. Data from - across five manufacturing sectors was analysed. The ARIMA model showed an R² of 0. 87 and mean absolute error (MAE) within the acceptable range, indicating high predictive accuracy with a ±3% uncertainty interval for cost projections. ARIMA models provide reliable forecasts for manufacturing costs in Senegal, suggesting potential improvements through targeted interventions. Implementing ARIMA-based forecasting can guide investment decisions and policy-making to enhance efficiency in manufacturing sectors. Manufacturing plants, Cost-effectiveness, Time-series analysis, ARIMA model, Forecasting Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.

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