In Senegalese industrial machinery fleets, maintenance costs can be significantly reduced through effective system reliability forecasting. A comprehensive analysis of historical failure data was conducted using ARIMA (AutoRegressive Integrated Moving Average) model to forecast future reliability trends. The ARIMA model demonstrated a strong predictive power with an accuracy rate of 85% in forecasting system failures over the next six months, providing actionable insights for maintenance planning. This study validates the effectiveness of time-series models in enhancing industrial machinery fleet reliability management in Senegal. Adoption of these forecasting tools can lead to substantial savings and improved operational efficiency within Senegalese industries. ARIMA, Time-series analysis, System reliability, Industrial maintenance, Senegal The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Sow et al. (Sat,) studied this question.
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