Forecasting System Reliability in Senegalese Industrial Machinery Fleets Using Time-Series Models
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| Format: | Recurso digital |
| Langue: | anglais |
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2004
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| _version_ | 1866901573224890368 |
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| author | Sow, Diop Gueye, Ibrahima Ndiaye, Mamadou |
| author_facet | Sow, Diop Gueye, Ibrahima Ndiaye, Mamadou |
| contents | <p>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 $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18706642 |
| institution | Zenodo |
| language | eng |
| publishDate | 2004 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Forecasting System Reliability in Senegalese Industrial Machinery Fleets Using Time-Series Models Sow, Diop Gueye, Ibrahima Ndiaye, Mamadou African Geography Time-Series Analysis Reliability Engineering Maintenance Optimization Predictive Maintenance Econometrics Stochastic Models <p>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 $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.</p> |
| title | Forecasting System Reliability in Senegalese Industrial Machinery Fleets Using Time-Series Models |
| topic | African Geography Time-Series Analysis Reliability Engineering Maintenance Optimization Predictive Maintenance Econometrics Stochastic Models |
| url | https://doi.org/10.5281/zenodo.18706642 |