African Maintenance Engineering | 24 July 2004
Forecasting System Reliability in Senegalese Industrial Machinery Fleets Using Time-Series Models
D, i, o, p, S, o, w, ,, I, b, r, a, h, i, m, a, G, u, e, y, e, ,, M, a, m, a, d, o, u, N, d, i, a, y, e
Abstract
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 $Y<em>{it}=\beta</em>0+\beta<em>1X</em>{it}+u<em>i+\varepsilon</em>{it}$, with robustness checked using heteroskedasticity-consistent errors.